Trending December 2023 # 40 Questions To Test Your Skill In Python For Data Science # Suggested January 2024 # Top 13 Popular

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Python is increasingly becoming popular among data science enthusiasts, and for right reasons. It brings the entire ecosystem of a general programming language. So you can not only transform and manipulate data, but you can also create strong pipelines and machine learning workflows in a single ecosystem.

At Analytics Vidhya, we love Python. Most of us use Python as our preferred tool for machine learning. Not only this, if you want to learn Deep Learning, Python clearly has the most mature ecosystem among all other languages.

Learning Python is the first step in your Data Science Journey. Want to know what are the milestones in Data Science Journey and how to achieve them? Check out the complete Data Science Roadmap!

If you are learning Python for Data Science, this test was created to help you assess your skill in Python. This test was conducted as part of DataFest 2023. Close to 1,300 people participated in the test with more than 300 people taking this test.

Below are the distribution scores of the people who took the test:

You can access the final scores here. Here are a few statistics about the distribution.

Mean Score: 14.16

Median Score: 15

Mode Score: 0

Questions & Answers

Question Context 1

Below is the subtitle sample script.

Note: Python regular expression library has been imported as re.

txt = '''450 Okay, but, um, thanks for being with us. 451 But, um, if there's any college kids watching, 452 But, um, but, um, but, um, but, um, but, um, 453 We have to drink, professor. 454 It's the rules. She said "But, um" 455 But, um, but, um, but, um... god help us all. '''

1) Which of the following codes would be appropriate for this task?

A) len(re.findall(‘But, um’, txt))

B)‘But, um’, txt).count()

C) len(re.findall(‘[B,b]ut, um’, txt))

D)‘[B,b]ut, um’, txt)).count()

Solution: (C)

You have to find both capital and small versions of “but” So option C is correct.

Question Context 2

Suppose you are given the below string


In order to extract only the domain names from the email addresses from the above string (for eg. “aaa”, “bbb”..) you write the following code:

for i in re.finditer('([a-zA-Z]+)@([a-zA-Z]+).(com)', str):     print

2) What number should be mentioned instead of “__” to index only the domains?

Note: Python regular expression library has been imported as re.

A) 0

B) 1

C) 2

D) 3

Solution: (C)

Question Context 3

Your friend has a hypothesis – “All those people who have names ending with the sound of “y” (Eg: Hollie) are intelligent people.” Please note: The name should end with the sound of ‘y’ but not end with alphabet ‘y’.

Now you being a data freak, challenge the hypothesis by scraping data from your college’s website. Here’s data you have collected.

Name Marks

Andy 0

Mandi 10

Sandy 20

Hollie 18

Molly 19

Dollie 15

You want to make a list of all people who fall in this category. You write following code do to the same:

temp = [] for i in re.finditer(pattern, str):     temp.append(

3) What should be the value of “pattern” in regular expression?

Note: Python regular expression library has been imported as re.

D) None of these

Solution: (B)

You have to find the pattern the end in either “i” or “ie”. So option B is correct.

Question Context 4

Assume, you are given two lists:

a = [1,2,3,4,5]

b = [6,7,8,9]

The task is to create a list which has all the elements of a and b in one dimension.


a = [1,2,3,4,5,6,7,8,9]

4) Which of the following option would you choose?

A) a.append(b)B) a.extend(b)

C) Any of the above

D) None of these

Solution: (B)

Option B is correct


5) You have built a machine learning model which you wish to freeze now and use later. Which of the following command can perform this task for you?

A) push(model, “file”)B) save(model, “file”)C) dump(model, “file”)D) freeze(model, “file”)

Solution: (C)

Option C is correct

A) push(model, “file”)B) save(model, “file”)C) dump(model, “file”)D) freeze(model, “file”)

Question Context 6

We want to convert the below string in date-time value:

import time str = '21/01/2023' datetime_value = time.strptime(str,date_format)

6) To convert the above string, what should be written in place of date_format?A) “%d/%m/%y”

B) “%D/%M/%Y”

C) “%d/%M/%y”

D) “%d/%m/%Y”

Solution: (D)

Option D is correct

Question Context 7

I have built a simple neural network for an image recognition problem. Now, I want to test if I have assigned the weights & biases for the hidden layer correctly. To perform this action, I am giving an identity matrix as input. Below is my identity matrix:

0, 0, 1]7) How would you create this identity matrix in python?

Note: Library numpy has been imported as np.A) np.eye(3)

B) identity(3)

C) np.array([1, 0, 0], [0, 1, 0], [0, 0, 1])

D) All of these

Option B does not exist (it should be np.identity()). And option C is wrong, because the syntax is incorrect. So the answer is option A

8) To check whether the two arrays occupy same space, what would you do?

I have two numpy arrays “e” and “f”.You get the following output when you print “e” & “f”

print e [1, 2, 3, 2, 3, 4, 4, 5, 6] print f [[1, 2, 3], [2, 3, 4], [4, 5, 6]]

When you change the values of the first array, the values for the second array also changes. This creates a problem while processing the data.

For example, if you set the first 5 values of e as 0; i.e.

print e[:5] 0

the final values of e and f are

print e [0, 0, 0, 0, 0, 4, 4, 5, 6] print f [[0, 0, 0], [0, 0, 4], [4, 5, 6]]

You surmise that the two arrays must have the same space allocated.

A) Check memory of both arrays, if they match that means the arrays are same.

B) Do “np.array_equal(e, f)” and if the output is “True” then they both are same

C) Print flags of both arrays by e.flags and f.flags; check the flag “OWNDATA”. If one of them is False, then both the arrays have same space allocated.

D) None of these

Solution: (C)

Option C is correct

Question Context 9

Suppose you want to join train and test dataset (both are two numpy arrays train_set and test_set) into a resulting array (resulting_set) to do data processing on it simultaneously. This is as follows:

train_set = np.array([1, 2, 3]) test_set = np.array([[0, 1, 2], [1, 2, 3]])

9) How would you join the two arrays?

Note: Numpy library has been imported as npA) resulting_set = train_set.append(test_set)

B) resulting_set = np.concatenate([train_set, test_set])

C) resulting_set = np.vstack([train_set, test_set])

D) None of these

Solution: (C)

Both option A and B would do horizontal stacking, but we would like to have vertical stacking. So option C is correct

Question Context 10

Suppose you are tuning hyperparameters of a random forest classifier for the Iris dataset.

Sepal_length Sepal_width Petal_length Petal_width Species 4.6 3.2 1.4 0.2 Iris-setosa 5.3 3.7 1.5 0.2 Iris-setosa 5.0 3.3 1.4 0.2 Iris-setosa 7.0 3.2 4.7 1.4 Iris-versicolor 6.4 3.2 4.5 1.5 Iris-versicolor

10) What would be the best value for “random_state (Seed value)”?

A) np.random.seed(1)B) np.random.seed(40)

C) np.random.seed(32)

D) Can’t say

Solution: (D)

There is no best value for seed. It depends on the data.

Question 11

While reading a csv file with numpy, you want to automatically fill missing values of column “Date_Of_Joining” with date “01/01/2010”.

Name Age Date_Of_Joining Total_Experience Andy 20 01/02/2013 0 Mandy 30 01/05/2014 10 Sandy 10 0 Bandy 40 01/10/2009 20

11) Which command will be appropriate to fill missing value while reading the file with numpy? 

Note: numpy has been imported as np

temp = np.genfromtxt(filename, filling_values=filling_values)

temp = np.loadtxt(filename, filling_values=filling_values)

temp = np.gentxt(filename, filling_values=filling_values)

D) None of these

Solution: (A)

Option A is correct

12) How would you import a decision tree classifier in sklearn?

A) from sklearn.decision_tree import DecisionTreeClassifierB) from sklearn.ensemble import DecisionTreeClassifier

C) from chúng tôi import DecisionTreeClassifier

D) None of these

Solution: (C)

Option C is correct

Note: Library StringIO has been imported as StringIO.

A) data = pd.read_csv(source) B) data = pd.read_csv(source)


data = pd.read_csv(source)

D) None of these

Solution: (A)

Option A is correct

Question Context 14

Imagine, you have a dataframe train file with 2 columns & 3 rows, which is loaded in pandas.

import pandas as pd

train  = pd.DataFrame({'id':[1,2,4],'features':[["A","B","C"],["A","D","E"],["C","D","F"]]})

Now you want to apply a lambda function on “features” column: 

train['features_t'] = train["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))

14) What will be the output of following print command?

print train['features_t']


2    C D F


0    AB

1    ADE

2    CDF

D) None of these

Solution: (A)

Option A is correct

Question Context 15

We have a multi-class classification problem for predicting quality of wine on the basis of its attributes. The data is loaded in a dataframe “df”

fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates Alcohol quality

0 7.4 0.70 0.00 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 5

1 7.8 0.88 0.00 2.6 0.098 25 67 0.9968 3.20 0.68 9.8 5

2 7.8 0.76 0.04 2.3 0.092 15 54 0.9970 3.26 0.65 9.8 5

3 11.2 0.28 0.56 1.9 0.075 17 60 0.9980 3.16 0.58 9.8 6

4 7.4 0.70 0.00 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 5

The quality column currently has values 1 to 10, but we want to substitute this by a binary classification problem. You want to keep the threshold for classification to 5, such that if the class is greater than 5, the output should be 1, else output should be 0.

15) Which of the following codes would help you perform this task?

Note: Numpy has been imported as np and dataframe is set as df.A)

Y = df[quality].values


Y = df[quality].values()


Y = df[quality]

D)None of these

Solution: (A)

Option A is correct

Question Context 16

Suppose we make a dataframe as

df = pd.DataFrame(['ff', 'gg', 'hh', 'yy'],              [24, 12, 48, 30],               columns = ['Name', 'Age'])

16) What is the difference between the two data series given below?

df[‘Name’] and

df.loc[:, ‘Name’]

Note: Pandas has been imported as pd

A) 1 is view of original dataframe and 2 is a copy of original dataframe.

B) 2 is view of original dataframe and 1 is a copy of original dataframe.

C) Both are copies of original dataframe.

D) Both are views of original dataframe

Solution: (B)

Option B is correct. Refer the official docs of pandas library.

Question Context 17

Consider a function “fun” which is defined below:

def fun(x):     x[0] = 5     return x

Now you define a list which has three numbers in it.

g = [10,11,12]

17) Which of the following will be the output of the given print statement:

print  fun(g), g

A) [5, 11, 12] [5, 11, 12]B) [5, 11, 12] [10, 11, 12]

C) [10, 11, 12] [10, 11, 12]

D) [10, 11, 12] [5, 11, 12]

Solution: (A)

Option A is correct

Question Context 18

Sigmoid function is usually used for creating a neural network activation function. A sigmoid function is denoted as

def sigmoid(x):   return (1 / (1 + math.exp(-x)))

18) It is necessary to know how to find the derivatives of sigmoid, as it would be essential for backpropagation. Select the option for finding derivative?


import scipy Dv = scipy.misc.derive(sigmoid)


from sympy import * x = symbol(x) y = sigmoid(x) Dv = y.differentiate(x)


Dv = sigmoid(x) * (1 - sigmoid(x))

D) None of these

Solution: (C)

Option C is correct

Question Context 19

Suppose you are given a monthly data and you have to convert it to daily data.

For example,

For this, first you have to expand the data for every month (considering that every month has 30 days)

19) Which of the following code would do this?

Note: Numpy has been imported as np and dataframe is set as df.A) new_df = pd.concat([df]*30, index = False)

B) new_df = pd.concat([df]*30, ignore_index=True)

C) new_df = pd.concat([df]*30, ignore_index=False)

D) None of these

Solution: (B)

Option B is correct

Context: 20-22

Suppose you are given a dataframe df.

What will be the output of print statement below?

print df.columns

Note: Pandas library has been imported as pd.

C) Error

D) None of these

Solution: (B)

Option B is correct

Context: 20-22

Suppose you are given a data frame df.

C) We cannot perform this task since dataframe and dictionary are different data structures

D) None of these 

Solution: (A)

Option A is correct

22) In above dataframe df. Suppose you want to assign a df to df1, so that you can recover original content of df in future using df1 as below.

df1 = df

Now you want to change some values of “Count” column in df.

Which of the following will be the right output for the below print statement?

print df.Count.values,df1.Count.values

Note: Pandas library has been imported as pd.

A) [200 200 300 400 250] [200 200 300 400 250]

B) [100 200 300 400 250] [100 200 300 400 250]

C) [200 200 300 400 250] [100 200 300 400 250]

D) None of these

Solution: (A)

Option A is correct

You copy whole code in an Ipython / Jupyter notebook, with each code line as a separate block and write magic function %%timeit in each block

A) 1 & 2

B) 1,2 & 3

C) 1,2 & 4

D) All of the above 

Solution: (C)

Option C is correct

24) How would you read data from the file using pandas by skipping the first three lines?

Note: pandas library has been imported as pd In the given file (email.csv), the first three records are empty.

,,, ,,, ,,, Email_Address,Nickname,Group_Status,Join_Year [email protected],aa,Owner,2014 [email protected],bb,Member,2023 [email protected],cc,Member,2023 [email protected],dd,Member,2023

A) read_csv(‘email.csv’, skip_rows=3)

B) read_csv(‘email.csv’, skiprows=3)

C) read_csv(‘email.csv’, skip=3)

D) None of these

Solution: (B)

Option B is correct

25) What should be written in-place of “method” to produce the desired outcome?

Given below is dataframe “df”:

Now, you want to know whether BMI and Gender would influence the sales.

For this, you want to plot a bar graph as shown below:

The code for this is:

var = df.groupby(['BMI','Gender']).Sales.sum() var.unstack().plot(kind='bar', method,  color=['red','blue'], grid=False)

A) stacked=True

B) stacked=False

C) stack=False

D) None of these

Solution: (A)

It’s a stacked bar chart.

26) Suppose, you are given 2 list – City_A and City_B.

City_A = [‘1′,’2′,’3′,’4’]

City_B = [‘2′,’3′,’4′,’5’]

In both cities, some values are common. Which of the following code will find the name of all cities which are present in “City_A” but not in “City_B”.

A) [i for i in City_A if i not in City_B]B) [i for i in City_B if i not in City_A]

C) [i for i in City_A if i in City_B]

D) None of these

Solution: (A)

Option A is correct

Question Context 27

Suppose you are trying to read a file “temp.csv” using pandas and you get the following error.

Traceback (most recent call last): UnicodeEncodeError: 'ascii' codec can't encode character.

27) Which of the following would likely correct this error?

Note: pandas has been imported as pdA) pd.read_csv(“temp.csv”, compression=’gzip’)

B) pd.read_csv(“temp.csv”, dialect=’str’)

C) pd.read_csv(“temp.csv”, encoding=’utf-8′)

D) None of these

Solution: (C)

Option C is correct, because encoding should be ‘utf-8’

28) Suppose you are defining a tuple given below:

tup = (1, 2, 3, 4, 5 )

Now, you want to update the value of this tuple at 2nd index to 10. Which of the following option will you choose?

A) tup(2) = 10

B) tup[2] = 10

C) tup{2} = 10

D) None of these

Solution: (D)

A tuple cannot be updated.

C) Both A and B

D) None of these

Solution: (C)

Option C is correct

Question Context 30

Suppose you are given the below web page

html_doc = “”” ...  “””

30) To read the title of the webpage you are using BeautifulSoup. What is the code for this? 

Hint: You have to extract text in title tag


    print soup.title.string

    print soup.title.get_text

    None of these

    Solution: (B)

    Question Context 31

    Imagine, you are given a list of items in a DataFrame as below.

    D = [‘A’,’B’,’C’,’D’,’E’,’AA’,’AB’]

    Now, you want to apply label encoding on this list for importing and transforming, using LabelEncoder.

    from sklearn.preprocessing import LabelEncoder le = LabelEncoder()

    31) What will be the output of the print statement below ?

    print le.fit_transform(D)

      array([0, 2, 3, 4, 5, 6, 1])

      array([0, 3, 4, 5, 6, 1, 2])

      array([0, 2, 3, 4, 5, 1, 6])

      Any of the above

      Solution: (D)

      Option D is correct

      32) Which of the following will be the output of the below print statement?

      print chúng tôi == np.nan

      Assume, you have defined a data frame which has 2 columns.

      import numpy as np df = pd.DataFrame({'Id':[1,2,3,4],'val':[2,5,np.nan,6]})

      3    False

      3    False

      3    True

      D) None of these

      Solution: (A)

      Option A is correct

      33) Suppose the data is stored in HDFS format and you want to find how the data is structured. For this, which of the following command would help you find out the names of HDFS keys?

      Note: HDFS file has been loaded by h5py as hf.

      A) hf.key()

      B) hf.key

      C) hf.keys()

      D) None of these

      Solution: (C)

      Option C is correct

      Question Context 34

      You are given reviews for movies below:

      reviews = [‘movie is unwatchable no matter how decent the first half is  . ‘, ‘somewhat funny and well  paced action thriller that has jamie foxx as a hapless  fast  talking hoodlum who is chosen by an overly demanding’, ‘morse is okay as the agent who comes up with the ingenious plan to get whoever did it at all cost .’]

      Your task is to find sentiments from the review above. For this, you first write a code to find count of individual words in all the sentences.

      counts = Counter() for i in range(len(reviews)): for word in reviews[i].split(value): counts[word] += 1

      34)What value should we split on to get individual words?

        ‘ ‘



        None of these

        Solution: (A)

        Option A is correct

        35) How to set a line width in the plot given below?

        For the above graph, the code for producing the plot was

        import matplotlib.pyplot as plt plt.plot([1,2,3,4])

          In line two, write plt.plot([1,2,3,4], width=3)

          In line two, write plt.plot([1,2,3,4], line_width=3

          In line two, write plt.plot([1,2,3,4], lw=3)

          None of these

          Solution: (C)

          Option C is correct

          36) How would you reset the index of a dataframe to a given list? The new index is given as:

          new_index=[‘Safari’,’Iceweasel’,’Comodo Dragon’,’IE10′,’Chrome’]

          Note: df is a pandas dataframe


          Firefox 200 0.04

          Chrome 200 0.02

          Safari 404 0.07

          IE10 404 0.08

          Konqueror 301 1.00

          A) df.reset_index(new_index,)

          B) df.reindex(new_index,)

          C) df.reindex_like(new_index,)

          D) None of these

          Solution: (A)

          Option A is correct

          37) Determine the proportion of passengers survived based on their passenger class.

          PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked

          0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S

          1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th… female 38.0 1 0 PC 17599 71.2833 C85 C

          2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S

          3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S

          4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

            crosstab(df_train[‘Pclass’], df_train[‘Survived’])

            proportion(df_train[‘Pclass’], df_train[‘Survived’])

            crosstab(df_train[‘Survived’], df_train[‘Pclass’])

            None of these

            Solution: (A)

            Option A is correct

            38) You want to write a generic code to calculate n-gram of the text. The 2-gram of this sentence would be [[“this, “is”], [“is”, “a”], [“a, “sample”], [“sample”, “text”]]

            Which of the following code would be correct?

            ‘this is a sample text’.

              output = [] return output

              output = [] return output

              output = [] return output

              None of these

              Solution: (B)

              Option B is correct

              39) Which of the following code will export dataframe (df) in CSV file, encoded in UTF-8 after hiding index & header labels.




                None of these

                Solution: (C)

                Option C is correct

                40) Which of the following is a correct implementation of mean squared error (MSE) metric?

                Note: numpy library has been imported as np.

                  return np.mean((np.square(real_target) – np.square(predicted_target)))

                  return np.mean((real_target – predicted_target)**2)

                  return np.sqrt(np.mean((np.square(real_target) – np.square(predicted_target))))

                  None of the above

                  Solution: (B)

                  Option B is correct

                  End Notes

                  If you are learning Python, make sure you go through the test above. It will not only help you assess your skill. You can also see where you stand among other people in the community. If you have any questions or doubts, feel free to post them below.


                  You're reading 40 Questions To Test Your Skill In Python For Data Science

                  3 Data Skill Sets You Need To Succeed In Data Seo

                  Data SEO is a scientific approach to search optimization that relies on the analysis and activation of data to make decisions.

                  But that’s not all it entails.

                  If you want your organization to succeed in data SEO, there are three distinct specializations you need to develop in addition to SEO knowledge and experience.

                  These are the skill sets of the data scientist, data analyst, and data engineer.

                  Whatever your budget, it is possible to improve your SEO with a data-backed approach. In fact, the concepts used by data scientists are becoming increasingly accessible.

                  Here are the skill sets you need to make data SEO a part of your repertoire.

                  1. The Data Engineer

                  Data engineers are the professionals who prepare the company’s foundational big data infrastructure.

                  They are often software engineers who design, build, integrate data from various resources and manage large amounts of data.

                  Their main goal is to optimize performance where it comes to the company’s access to its own data.

                  In large companies, data engineers work with a legal manager for GDPR or CCPA compliance, and often with a security manager.

                  They frequently use ETL (Extract, Transform and Load) to centralize data, creating large data warehouses that can be used for reporting or analysis.

                  The main skills and tools can be summarized in the following list:





                  Data streaming.




                  Why Should You Centralize Your Data?

                  First of all, you don’t have infinite time available. Not only is it a waste of time to juggle between tools, but it is also a waste of information not to be able to combine data from different sources.

                  Often, you have to combine your data with business data (CRM), finance data, and many other types of data that always come with access and security concerns.

                  Therefore, it is wise to build your SEO data warehouse by ensuring that your SEO tools allow you to export the data properly.

                  However, there are many difficulties.

                  The first difficulty concerns the volume of information.

                  If you have more than 100,000 pages on your website and a lot of web traffic, weekly crawls and daily logs will quickly take up a lot of space.

                  This becomes even more complex if you add your CRM data and data on your competitors.

                  And if the system is not based on the right technologies you can have incomplete, missing, or false data.

                  There are many traps in addition to the volume of data.

                  These include currency concerns if you work internationally, where you will have to deal with the exchange rates issued each day by the authoritative financial institution in your country.

                  They might also include time differences. If you calculate a turnover per day in France and that a part of the turnover takes place in Canada, for example, you have to launch the calculation when it is midnight in Canada and not midnight in France.

                  These are just a couple of examples, but every business is full of traps.

                  Next, you have to keep a close eye on the veracity of the data because data can be corrupted quickly:

                  A JavaScript script for GA disappears and your traffic data becomes erroneous.

                  An API changes its return parameters and several fields no longer obtain a value.

                  A database is no longer updated because the hard disk is full.

                  No matter what the case, you must quickly detect this type of anomaly and correct it as soon as possible.

                  Otherwise, the dashboards produced by this data will be erroneous. It’s tedious and time-consuming to launch retroactive scripts to recalculate everything.

                  If you don’t have a data engineer on your team, you must at least have a manager who verifies the consistency of the data you retrieve from the different SEO tools.

                  SEO tools now allow you to easily pull the following data, which you need to monitor for variations up or down:

                  Analytics data: lost script, tracking error.

                  Crawl data: crawl too long, crawl canceled.

                  Server log data: missing periods.

                  Keyword tools data: adding new keywords.

                  Communication is key. With good incident management, the whole data chain becomes coherent for use by SEO experts, data analysts, and SEO consultants.

                  2. The Data Scientist

                  The data scientist will enrich the data with statistical models, machine learning, or analytical approaches.

                  Their main mission is to help the company transform the data made available by the data engineers into valuable and exploitable information.

                  Compared to data analysts (see below), data scientists must have strong programming skills to design new algorithms, as well as good business knowledge.

                  They must be able to explain, justify and communicate results to non-scientists.

                  Which Languages Should Be Used & Which Methodology?

                  The most popular technologies in 2023 for data science are, in the order of popularity:

                  If you can’t decide on a programming language, I can give you some tips.

                  First of all, use the most popular language in your company.

                  If the majority of the developers are using Python, there’s no need to push for R because trying to maintain code in R will double the maintenance cost. This way, you show your ability to adapt.

                  Then, let the technologies on which you want to deploy your applications guide your choice.

                  For example, if your team produces its dashboards with Shiny, then R will become your best friend.

                  After that, note that R and Python are relatively similar if you compare them to C or to Scala. If you’re building your CV, it is ideal to master both.

                  As far as methodology is concerned, the scientific method prevails and leaves no room for empiricism.

                  You want to clearly define the context and objectives, then explain the different methods identified and present reproducible results.

                  Finally, it’s entirely possible that you don’t have the time or the vocation to do data science yourself. In this case, I recommend using a service provider.

                  Regardless of the agency, the deliverables and criteria for success must be clearly defined with the chosen agency so that there are no unpleasant surprises when using the solution.

                  Additionally, you may also need to consider data science platforms. The options available to you will vary widely depending on your budget.

                  3. The Data Analyst

                  Data analysts are business-oriented data professionals who can query and process data, provide reports, summarize and visualize data.

                  They know how to leverage existing tools and methods to solve a problem and help people across the company understand specific queries through ad hoc reporting and graphics.

                  They base their work on the data warehouses of data engineers and the results of the algorithms of data scientists.

                  Their skills are diverse and can include statistics, data mining, and data visualization.

                  What Software Should Be Used?

                  Data Studio is well known in the field of SEO but in business, the market is dominated by Tableau Software, SAP, Microsoft, and IBM.

                  The recent acquisition of Looker by Google positions it to be among the leaders in the years to come, as well.

                  Be careful in choosing a data visualization solution.

                  Data analysts’ ability to quickly adapt to tools brings us back to a “Make or Buy” issue. If you have the budget, proprietary solutions will save you a lot of time.

                  How to Create Perfect Dashboards

                  There are many methods but here is the SMART goals framework is easy to remember and can apply here, as well:

                  Keep charts specific and simple, as too much information kills the information.

                  The y-axis and x-axis must illustrate measurable data.

                  A graph should focus on achievable metrics, as there is no point in monitoring metrics that will have no influence on your business. Weather is an excellent example: it has a crucial role on some sites and none on others.

                  Dashboards should always have relevant summaries in order to be read quickly and understood. If it takes more than three seconds to understand them, you can improve the end result. First, users may be satisfied with an overview, but then they may need a more granular view of the data by juggling filters.

                  The most important data is time, so be sure to track time-based data comparing each day, month, year, etc.

                  Of course, keep in mind that if data analysts master SQL, they can turn to open source solutions like Metabase or Superset.

                  Finally, analysts with programming skills will want to look at Shiny for R or Dash for Python.

                  Data SEO Projects

                  The world of data SEO has certainly become less obscure.

                  As for any project, you will either need to surround yourself with the right people to succeed in large-scale data projects or be well-trained in the professional skillsets we covered in this article: data engineering, data analysis, data science.

                  At this point, you have probably identified weaknesses or strengths within your company while reading this article.

                  Don’t hesitate to build out on your weak points by recruiting, outsourcing or training.

                  More Resources:

                  Image Credits

                  All screenshots taken by author, May 2023

                  Top Data Science Jobs To Apply For In March 2023


                  Top data science jobs in March 2023 Data scientist at IBM

                  Location: India IBM India Pvt. Limited has been present in the country since 1992. IBM India’s solutions and services span all major industries including financial services, healthcare, government, automotive, telecommunication and education, among others. As a trusted partner with wide-ranging service capabilities, IBM helps clients transform and succeed in challenging circumstances. IBM has been expanding its footprint in India and has a presence in over 200 cities and towns across the country, either directly or through its strong business partner network.   Role and responsibility: The expected candidate should be a Subject Matter Expert on building big data solutions with great scalability. Led by a solution architect, they will provide expertise and leadership to help design IBM Data Science and AI solutions that will help the company’s clients drive technology benefits and business outcomes across industries. The candidate will work with cutting edge technologies such as Watson, as well as open-source approaches with a passionate team of people who are driving the innovation and digital transformation to cross-industry enterprise clients with the adoption of IBM Data Science and AI.   Apply for the job  

                  Data Scientist at HCL

                  Location: Noida, Delhi, India HCL Engineering and R&D Services (ERS) partners with global enterprises in accelerating product development by leveraging the latest technologies, monetizing product services, and providing an immersive customer experience. The company is one of the most valued broad-based global engineering service providers (ESPs). With over four decades of experience in supporting customers in their digital transformation, HCL seamlessly integrates with and complements customers’ product engineering and research and development activities. Apply for the job  

                  Data Scientist (Banking Domain) at Capgemini

                  Location: Chennai, Tamil Nadu, India Capgemini is a global leader in consulting, digital transformation, technology and engineering services. The Group is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital platforms. Building on its storing 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Role and Responsibility: The candidates applying for the data science job profile at Capgemini is expected to have 5 to 9 years of experience in the banking or IT industry on data science projects involving quantitative statistical and predictive analysis of large data. He/she should have prior knowledge of mathematical programming platforms/packages like R, Python, MATLAB and basic programming skills in Python or C. Apply for the job  

                  T&O- OA Data Scientist (Behavioral Science) at Accenture

                  Location: Gurugram, Haryana, India Role and responsibility: As an Organisational Analytics (OA) scientist in Accenture’s Talent & Organisation (T&O) practice, the candidate should fetch information from various sources and analyze it to better understand people behaviour. He/she should fetch information from various sources related to Behavioural science/industry and Organisational Psychology/Social Sciences to create new frameworks. The candidate should also design a framework for the questionnaire and other assessment. Apply for the job  

                  Principal Data Scientist at DirecTech Labs

                  Location: Noida, India DirecTech Labs was created to tap an incredible opportunity for its clients by helping the industry grow through improving experience and satisfaction for consultants. The company uses data to uncover previously invisible patterns of seller and customer behaviour and unprecedented insights into direct sellers at an individual level. Role and Responsibility: As a principal data scientist, the candidate gets a chance to drive the behavioural intelligence that powers DirecTech Labs actionable analytics and global messaging platform with over a million users and 20 million lifecycles in its database. He/she will be a key member of the data science team working to improve current behavioural models, develop new ones, and prove the efficacy and business impact of the company’s global messaging and alerts. Apply for the job  

                  Data Science-Lead at Wipro

                  Location: Bengaluru, Karnataka, India Wipro Limited is a leading global information technology, consulting and business process service company. Wipro harnesses the power of cognitive computing, hyper-automation, robotics, cloud, analytics and emerging technologies to help the company’s clients adapt to the digital world ad make them successful. Role and responsibility: The profile demands mandatory knowledge in data science and Python. As a lead, he/she is responsible for managing a small team of analysts, developers, testers or engineering and drive delivery of a small module within a project. Apply for the job  

                  Data Analyst at Amazon

                  Location: Bengaluru, Karnataka, India Amazon is the world’s largest online retailer and a prominent cloud services provider. Amazon aims to be earth’s most customer-centric company. Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational experience, and long-term thinking. The company is driven by the excitement of building technologies, inventing products, and providing services that change lives. Roles and responsibility: As an analyst, the candidate will work closely with program and product managers to analyze data, identify, areas to improve, define metrics to measure and monitor programs and build end-to-end reporting solutions which help leaders keep an eye on business areas. The candidate will also work closely with internal business teams to extract or mine information from Amazon’s existing systems to create new analysis, and expose data from its group to wider teams in intuitive ways.

                  Complete Guide To Moment Generating Functions In Statistics For Data Science

                  This article was published as a part of the Data Science Blogathon


                  While dealing with Statistical Moments (for a particular probability distribution either Continuous or Discrete) such as Mean, Variance, Skewness, etc, it becomes very important to have a good understanding of Moment Generating Functions (MGF).

                  So, In this article, we will be discussing the complete idea behind Moment Generating Functions including its applications with some examples.


                  Table of Contents

                  1. What are Statistical Moments?

                  2. What is Moment Generating Function (MGF)?

                  3. Properties of MGF

                  4. Why do we need MGF?

                  5. Some Important Results of MGF

                  6. Applications of MGF

                  7. Problem Solving related to MGF

                  What are Statistical Moments?

                  Let’s X be a random variable in which we are interested, then the moments are the expected values of X, 

                  For Example, E(X), E(X²), E(X³), … etc.

                  The first moment is defined as E(X),

                  The second moment is defined as E(X²),

                  The third moment is defined as E(X³),

                  The n-th moment is defined as E(Xn).

                  In Statistics, we are pretty familiar with the first two moments:

                  The Mean (μ) = E(X)

                  The Variance (σ2) = E(X2) – (E(X))2 = E(X²) − μ²

                  These are the important characteristics for any general random variable X.

                  The mean denotes the average value and the variance represents how the data points are spread wrt mean in the distribution. But there must be other characteristics as well that also helps in defining the probability distributions.

                  For Example, In the third moment E(X3), Skewness tells about the asymmetry of distribution, and in the fourth moment E(X4), kurtosis tells about how heavy the tails of a distribution are.

                                                                       Image Source: Google Images

                  What is the Moment Generating Function?

                  The moment generating function (MGF) associated with a random variable X, is a function,

                  MX : R → [0,∞] defined by

                   MX(t) = E [ etX ]

                  In general, t can be a complex number, but since we did not define the expectations for complex-valued random variables, so we will restrict ourselves only to real-valued t. And the point to note that t= 0 is always a point in the ROC for any random variable since MX (0) = 1.

                  As its name implies, MGF is the function that generates the moments —

                  E(X), E(X²), E(X³), …, E(Xn).


                  If X is discrete with probability mass function(pmf) pX (x), then

                  MX (t) = Σ etx pX (x)

                  If X is continuous with probability density function (pdf) fX (x), then

                   MX (t) = ∫ etx fX (x) dx

                  Properties of Moment Generating Functions

                  1. Condition for a Valid MGF:

                  MX(0) = 1 i.e, Whenever you compute an MGF, plug in t = 0 and see if you get 1.

                  2. Moment Generating Property:

                  By looking at the definition of MGF, we might think that how we formulate it in the form of E(Xn) instead of E(etx). 

                  So, to do this we take a derivative of MGF n times and plug t = 0 in. then, you will get E(Xn).

                                                                Image Source: Google Images


                  To prove the above property, we take the help of Taylor’s Series:

                  Step-1: Let’s see Taylor’s series Expansion of eX and then by using that expansion, we generate the expansion for etX which we will use in later steps.

                  Step-2: Take the expectation on both sides of the equation, we get:

                  Step-3: Now, take the derivative of the equation with respect to t and then we will reach our conclusion.

                  In this step, we take the first derivative of the equation only but similarly, we can prove that:

                  If you take another derivative on equation-3 (therefore total twice), you will get E(X²).

                  If you take the third derivative, you will get E(X³), and so on.


                  When you try to deeply understand the concept behind the Moment Generating Function, we couldn’t understand the role of t in the function, since t seemed like some arbitrary variable that we are not interested in. However, as you see, t is considered as a helper variable.

                  So, to be able to use calculus (derivatives) and make the terms (that we are not interested in) zero, we introduced the variable t.

                  Why do we need MGF?

                  We can calculate moments by using the definition of expected values but the question is that “Why do we need MGF exactly”?

                                                                          Image Source: Google Images

                  For convenience,

                  To calculate the moments easily, we have to use the MGF. But

                  “Why is the calculation of moments using MGF easier than by using the definition of expected values”?

                  Let’s understand this concept with the help of the given below example that will cause a spark of joy in you — the clearest example where MGF is easier: 

                  Let’s try to find the MGF of the exponential distribution.

                  Step-1: Firstly, we will start our discussion by writing the PDF of Exponential Distribution.

                  Step-2: With the help of pdf calculated in previous steps, now we determine the MGF of the exponential distribution.

                  Now, for MGF to exist, the expected value E(etx) should exist.

                  Therefore, `t – λ < 0` becomes an important condition to meet, because if this condition doesn’t hold, then the integral won’t converge. This is known as the Divergence Test.

                  Once you have to find the MGF of the exponential distribution to be λ/(λ-t), then calculating moments becomes just a matter of taking derivatives, which is easier than the integrals to calculate the expected value directly.

                                                                       Image Source: Google Images

                  Therefore, with the help of MGF, it is possible to find moments by taking derivatives rather than doing integrals! So, this makes our life easier when dealing with statistical moments.

                  Important Results Related to MGF

                  Result-1: Sum of Independent Random Variables

                  Suppose X1,…, Xn are n independent random variables, and the random variable Y is defined by

                  Y = X1 + … + Xn.

                  Then, the moment generating function of random variable Y is given as,

                  MY(t)=MX1 (t)·…·MXn (t)


                  Suppose for two random variables X and Y we have MX(t) = MY (t) < ∞ for all t in an interval, then X and Y have the same distribution.

                  Applications of MGF

                  1. Moments provide a way to specify a distribution:

                  We can completely specify the normal distribution by the first two moments, mean and variance. As we are going to know about multiple different moments of the distribution, then we will know more about that distribution.

                  For Example, If there is a person that you haven’t met, and you know about their height, weight, skin color, favorite hobby, etc., you still don’t necessarily fully know them but to getting more and more about them we can take the help of this.

                  2. Finding any n-th moment of a distribution:

                  We can get any n-th moment once you have MGF i.e, expected value exists. It encodes all the moments of a random variable into a single function from which we can be extracted again later.

                  3. Helps in determining Probability distribution uniquely:

                  Using MGF, we can uniquely determine a probability distribution. If two random variables have the same expression of MGF, then they must have the same probability distribution.

                  4. Risk Management in Finance: 

                  In this domain, one of the important characteristics of distribution is how heavy its tails are.

                  For Example, If you know about the 2009 financial crisis, in which we were failing to address the possibility of rare events happening. So, risk managers try to understate the kurtosis, the fourth moment of many financial securities underlying the fund’s trading positions. So, sometimes seemingly random distributions with the help of hypothetically smooth curves of risk can have hidden bulges in them. So, to detect these bulges we can use the MGF.

                  Problem Solving related to MGF

                  Numerical Example: 

                  Suppose that Y is a random variable with MGF H(t). Further, suppose that X is also a random variable with MGF M(t) which is given by, M(t) = 1/3 (2e3t +1) H(t). Given that the mean of random variable Y is 10 and its variance is 12, then find the mean and variance of random variable X.


                  Keep in mind all the results which we described above, we can say that

                  E(Y) = 10 ⇒ H'(0) =10,

                  E(Y2) – (E(Y))2 = 12 ⇒ E(Y2) – 100 = 12 ⇒ E(Y2) = 112 ⇒ H”(0) = 112

                  M'(t) = 2e3t H(t) + 1/3 ( 2e3t +1 )H'(t)

                  M”(t) = 6e3tH(t) + 4e3tH'(t) + 1/3 ( 2e3t +1 )H”(t)

                  Now, E(X) = M'(0) = 2H(0) + H'(0) = 2+10 =12

                  E(X2) = M”(0) = 6H(0) + 4H'(0) + H”(0) = 6 + 40 +112 = 158

                  Therefore, Var(X) = E(X2) – (E(X))2 = 158 -144 = 14

                  So, the mean and variance of Random variable X are 12 and 14 respectively.

                  This ends today’s discussion!



                  Thanks for reading!

                  I hope you enjoyed the article and increased your knowledge about Moment Generating Functions in Statistics.

                  Please feel free to contact me on Email

                  For the remaining articles, refer to the link.

                  About the Author Aashi Goyal

                  Currently, I am pursuing my Bachelor of Technology (B.Tech) in Electronics and Communication Engineering from Guru Jambheshwar University(GJU), Hisar. I am very enthusiastic about Statistics, and Data Science.

                  The media shown in this article on Moment Generating Functions are not owned by Analytics Vidhya and are used at the Author’s discretion.


                  Evaluating A Classification Model For Data Science

                  This article was published as a part of the Data Science Blogathon.

                  Machine Learning tasks are mainly divided into three types

                  Supervised Learning — In Supervised learning, the model is first trained using a Training set(it contains input-expected output pairs). This trained model can be later used to predict output for any unknown input.

                  Unsupervised Learning — In unsupervised learning, the model by itself tries to identify patterns in the training set.

                  Reinforcement Learning —  This is an altogether different type. Better not to talk about it.

                  Supervised learning task mainly consists of Regression & Classification. In Regression, the model predicts continuous variables whereas the model predicts class labels in Classification.

                  For this entire article, let’s assume you’re a Machine Learning Engineer working at Google. You are ordered to evaluate a handwritten alphabet recognizer. Train classifier model, training & test set are provided to you.

                  The first evaluation metric anyone would use is the “Accuracy” metric. Accuracy is the ratio of correct prediction count by total predictions made. But wait a minute . . .

                  Is Accuracy enough to evaluate a model?

                  Short answer: No

                  So why is accuracy not enough? you may ask

                  So there are four distinct possibilities as shown below

                  The above table is self-explanatory. But just for the sake of some revision let’s briefly discuss it.

                  If the model predicts “A” as an “A”, then the case is called True Positive.

                  If the model predicts “A” a “Not A”, then the case is called False Negative.

                  If the model predicts “Not A” as an “A”, then the case is called False Positive.

                  If the model predicts “Not A” as a “Not A”, then the case is called True Negative

                  Another easy way of remembering this is by referring to the below diagram.

                  As some of you may have already noticed, the Accuracy metric does not represent any information about False Positive, False Negative, etc. So there is substantial information loss as these may help us evaluate & upgrade our model.

                  Okay, so what are other useful evaluation metrics? Confusion Matrix for Evaluation of Classification Model

                  A confusion matrix is a n x n matrix (where n is the number of labels) used to describe the performance of a classification model. Each row in the confusion matrix represents an actual class whereas each column represents a predicted class.

                  2) Predicted Target labels

                  ## dummy example from sklearn.metrics import confusion_matrix y_true = ["cat", "ant", "cat", "cat", "ant", "bird"] y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]) >>> array([[2, 0, 0], [0, 0, 1],

                             [1, 0, 2]])

                  We will take a tiny section of the confusion matrix above for a better understanding.

                  Precision =  TP/(TP+FP)

                  2) Predicted Target labels

                  ## dummy example from sklearn.metrics import precision_score y_true = [0, 1, 1, 0, 1, 0] y_pred = [0, 0, 1, 0, 0, 1] precision_score(y_true, y_pred) >>> 0.5

                  Precision in itself will not be enough as a model can make just one correct positive prediction & return the rest as negative. So the precision will be 1/(1+0)=1. We need to use precision along with another metric called “Recall”.


                  Recall is also called “True Positive Rate” or “sensitivity”.

                  2) Predicted Target labels

                  ## dummy example from sklearn.metrics import recall_score y_true = [0, 1, 1, 0, 1, 0] y_pred = [0, 0, 1, 0, 0, 1] recall_score(y_true, y_pred) >>> 0.333333 Hybrid of both

                  There is another classification metric that is a combination of both Recall & Precision. It is called the F1 score. It is the harmonic mean of recall & precision. The harmonic mean is more sensitive to low values, so the F1 will be high only when both precision & recall are high.

                  2) Predicted Target labels

                  ## dummy example from sklearn.metrics import f1_score y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]] y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]] f1_score(y_true, y_pred, average=None) >>> array([0.66666667, 1. , 0.66666667]) Ideal Recall or Precision

                  We can play with the classification model threshold to adjust recall or precision. In reality, there is no ideal recall or precision. It all depends on what kind of classification task is it. For example, in the case of a cancer detection system, you’ll prefer having high recall & low precision. Whereas in the case of an abusive word detector, you’ll prefer having high precision but low recall.

                  Precision/Recall Trade-off

                  Sadly, increasing recall will decrease precision & vice versa. This is called Precision/Recall Trade-off.

                  Precision & Recall vs Threshold

                  We can plot precision & recall vs threshold to get information about how their value changes according to the threshold. Here below is a dummy graph example.

                  ## dummy example from sklearn.metrics import precision_recall_curve precisions, recalls, thresholds = precision_recall_curve(y_true, y_predicted) plt.plot(thresholds, precisions[:-1], "b--", label="Precision") plt.plot(thresholds, recalls[:-1], "g-", label="Recall")

                  As you can see as the threshold increases precision increases but at the cost of recall. From this graph, one can pick a suitable threshold as per their requirements.

                  Precision vs Recall

                  Another way to represent the Precision/Recall trade-off is to plot precision against recall directly. This can help you to pick a sweet spot for your model.

                  ROC Curve for Evaluation of Classification Model 

                  2. FPR is the ratio of Negative classes inaccurately being classified as positive.

                  FPR = FP/(FP+TN)

                  Below is a dummy code for ROC curve.

                  from sklearn.metrics import roc_curve fpr, tpr, thresholds = roc_curve(y_true, y_predicted) plt.plot(fpr, tpr, linewidth=2, label=label) plt.plot([0, 1], [0, 1], 'k--')

                  In the below example graph, we have compared ROC curves for SGD & Random Forest Classifiers.

                  ROC curve is mainly used to evaluate and compare multiple learning models. As in the graph above, SGD & random forest models are compared. A perfect classifier will transit through the top-left corner. Any good classifier should be as far as possible from the straight line passing through (0,0) & (1,1). In the above graph, you can observe that the Random Forest model is working better compared to SGD.  PR curve is preferred over ROC curve when either the positive class is rare or you prioritize more about False Positive.



                  The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion


                  Top 10 Github Repositories For Data Science


                  Data science is a collaborative scientific field of computing that has grown many folds in recent years and has become the powerhouse behind the business decisions made by organizations in today’s time, be it the FAANG’s or early-stage startups.

                  As the field has grown, so have the number of individuals pursuing this domain and the learning resources available on the internet. The premier resource to learn data science is GitHub among all these resources.

                  What is GitHub?

                  As the word itself, GitHub suggests a hub for over 73 million coders and developers to host and share codes in a cooperative and collaborative environment. It provides several features like access control, version control and continuous integration for every project and is the most prominent source code host globally with over 28 million public repositories. I have compiled the top 10 repositories for learning data science out of these.

                   To know more about GitHub, read here.

                  Freecodecamp is free to learn online coding community with a speciality in various domains. It provides several certifications on different code profiles, including Data Visualization Certification, Data Analysis with Python Certification and Machine Learning with Python Certification. Freecodecamp community also has a forum where users can get programming help and feedback on their projects. They also have a Youtube channel that contains free courses on Python, SQL, Machine Learning and many more.

                  TensorFlow is an open-source framework for Machine learning and Artificial Intelligence developed by Google Brain Team. The GitHub repository contains various resources to learn and enhance the TensorFlow and Machine Learn skills.

                  You can learn more about TensorFlow through TensorFlow tutorials. These tutorials are written in Jupyter notebooks and can be run directly on Google Colab requiring no setup.

                  It also provides state of the art models for Machine Learning in domains such as computer vision, NLP, and recommendation systems. They are highly optimized and efficient at the task they are designed to do, which enables them to use them directly and generate highly accurate results on their datasets.

                  This GitHub repository contains various algorithms coded exclusively in Python. It enlists a collection of codes on domains such as Machine learning, Neural Networks, Digital Image Processing and Computer Vision.

                  The Machine Learning sub-repository provides codes on several regression techniques such as linear and polynomial regression. They are usually used in predictive analysis for continuous data and are very useful for problems pertaining to stock price or house prediction. It also contains classification methods such as logistic regression and multi-layer perceptron used to predict data containing discrete values(where data is divided into many classes).

                  The neural network repository contains codes on backpropagation which deals with updating weights in neural network architecture, Convolutional Neural Network provides the machine human like ability to distinguish between different classes of images. One of the most common applications of the CNN architecture is “”oogle Lens””

                  The digital image repository contains codes on edge detection such as Canny edge detection. These types of techniques are more often used to isolate the edges in an environment capture. One of the most known applications is autonomous cars which rely on the same for determining the road linings.

                  The computer vision repository contains codes for pooling, a feature of CCNN’sthat is used to extract the highest rated features in an image for classification.

                  The above given GitHub repository provides an organized list of machine learning libraries, frameworks and tools in almost all the languages available. As most of Machine Learning development is done on Python, practitioners without Python as their background may find it difficult to adapt to it. So this makes this repository even more valuable as it transcends all the languages and promotes a collective development environment for Machine Learning.

                  In python, the libraries are provided on the following domains:

                  Further elaborating, Computer vision libraries include scikit-image, scikit-opt, face_recognition, neural dream and many more, NLP libraries include CLTK and NLTK which helps us to build models that are able to understand human language data, Machine learning libraries include scikit learn, pattern and prophet which was developed by Facebook and is one of the best models for time series data prediction, Data Visualization and analysis libraries include pandas, numpy and many more which are really helpful in modelling and transforming our datasets and finally neural network libraries include neural_talk, nn_builder which can build neural networks in one line!!.

                  Among them, one of the most popular libraries is scikit-learn which contains notebooks for several machine learning algorithms such as K-Nearest Neighbors, Support Vector Machines, Random Forest, K-Means and principal components analysis.

                  Through the pandas i-notebooks one can learn techniques such as data indexing, merging joining, aggregation and filling in missing values. All of this comes under data cleaning and preparation and is the most important part of the data analysis pipeline. In fact, without data cleaning and augmentation no amount of analysis thorough different algorithms would yield any valuable or sensible results.

                  Through Matplotlib notebooks people can learn about creating user-friendly bar graphs and charts which are really helpful in depicting analysis results in a user-friendly way.

                  This repository contains instances of the most used and widely used machine learning codes and algorithms implemented using Python explained along with the mathematics and logic working behind them. Also, each algorithm is explained through Jupyter notebook’sinteractive environment. The codes are not only run on a training set for data analysis but also the mathematics is explained which makes it one of the best resources to strengthen one’s basics.

                  For supervised learning it provides assistance for regression and classification techniques by explaining the mathematics behind linear regression, logistic regression providing the code for it and running it on Jupyter notebook.

                  For unsupervised learning, it provides codes for clustering which is used in problems such as customer segmentation. In clustering, we split the training examples into different clusters based on columns of data whose legends are not known to us.

                  For neural network it provides an explanation on multi-layer perceptron, working of activation functions, cost functions, loss functions and gradient descent.

                  This GitHub repository is very important for those who want to understand the basics of data science and Machine Learning. It takes you from answering your elementary questions such as “”hat is data science”” “”hy we need to use it”” “”hat are its applications””and brings you to a position where you would be well versed with the basics of data science.

                  It also contains a curated list of MMOOC’s which is in my opinion one of the best ways one can gain knowledge in this domain.

                  It also contains several tutorials and free courses for you to start your data science journey.

                  It also contains a list of libraries used for deep learning, machine learning, tensorflow, Keras which are extensively used in each and every code you would come across in data science.

                  Also, you can find top journals, publications and magazines on data science and Big Data which is really helpful to remain up to date with the latest developments in the field.

                  For those who prefer listening over reading, you are in luck as it contains an exclusive list of podcasts and YouTube Channels on several data science topics such as AI, Big data and data engineering.

                  You can also follow up on reading the most popular books on data science and exchange your ideas and follow the most prominent bloggers.

                  As the name suggests deep learning drizzle is a GitHub repository dedicated to deep learning algorithms. It provides resources such as lecture slides of the most prominent universities of the world and their YouTube lectures on several domains such as:

                  Deep Neural Networks

                  Machine Learning Fundamentals

                  Natural Language Processing

                  Optimization for Machine Learning

                  General Machine Learning

                  Modern Computer Vision and many more.

                  These resources are highly valued and followed by millions of people across the globe. Thus they are bound to provide you with extensive knowledge of deep neural architecture and machine learning in general.

                  One of the major parts of learning any field be it data science, AI or any other is to have hands-on knowledge, to have practical experience. Most of the people studying or pursuing their interests in this field come across an opportunity to create projects on data science. So, this repository provides you with one of the most important lists containing over 500 projects on machine learning, NLP, AI along code. This is really helpful for those who want hands-on knowledge or want to create projects for their resume.

                  This repository contains interactive tools for deep learning, machine learning along with an explanation of the math behind it. It is really intuitive and a new way to understand and comprehend the complex nature of these algorithms. Their work is depicted through videos which help to see how they are converting and analyzing the data in real-time.

                  Take for instance the CNN Explainer, which is an interactive video description that explains the working of a convolutional network. And for each of these examples the code, demo d official paper are given.

                  Through the medium of this article, we have journeyed through the catalogue of the best GitHub repositories on theInternett. From free resources to interactive tools and from free courses to awesome codes, we have gone through some amazing work developed and provided to us for the taking. I am sure that, even if one imbibes a part of the assortment of these resources, she/he can excel and reach newer heights at their career in data science.



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