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Today, with the increasing volume of data day by day, processing and analysis of that data have become significant. This is where data scientists come into the scenario, determining the right data sets and variables and deriving actionable insights from that.
Data Scientists-Turned-CEOsThere are a number of data scientists who became CEOs making data a core part of their strategy, operations, and decision-making process.
Becoming Data-Driven CEOToday, with the increasing volume of data day by day, processing and analysis of that data have become significant. This is where data scientists come into the scenario, determining the right data sets and variables and deriving actionable insights from that. Data scientists are analytical data experts and their roles can vary organization to organization as per companies’ requirements. Since data is the new currency for all-sized businesses, the role of data scientists is continuously becoming essential, even some are turned to forge their own business becoming CEOs and putting heavy emphasis on data strategy. According to a report from Telsyte, big data analytics is playing a significant role in empowering CEOs and boards to drive the innovation agenda. There is also a growing realization that the information age is rapidly transforming traditional decision-making. In the next few years, the report noted, Australian organisations will have at least one executive in their team, if not the CEO, specialising in big data.There are a number of data scientists who became CEOs making data a core part of their strategy, operations, and decision-making process. Sebastian Thrun , who led the integration of big data into robotics, is the founder of edtech startup Udacity. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more. Brad Peters , a data scientist-turned-CEO, who founded business intelligence startup Birst. Before starting his own business, Brad led analytics at Siebel System. Jim Goodnight , the CEO of SAS, the world’s leading business analytics software vendor, has led the company since its inception in 1976. He earned his bachelor’s degree in applied mathematics and his master’s in statistics from North Carolina State University (NCSU). He also earned his doctorate in statistics at NCSU, where he was a faculty member from 1972 – 1976. Thomas Thurston is the founder and CEO of Growth Science, which uses data to predict if businesses will survive or fail. According to Thomas, a background in data science tends to help CEOs ask better questions and get better feedback, because it brings conversations down to a level of reality and practicality. Facts, data, and probabilities can have a way of removing the ego, politics, and hand-waving from a conversation. Considering these leaders, this is clear that data scientists are moving into CEO roles and helming most successful data startups, instead of choosing a well-paid position at a large company.A data-driven CEO leverage a various number of sources of data to make decisions with precision. CEOs leading successful data-driven companies must adopt data-driven skill sets, processes, and cultures. While businesses worldwide are trying to make more effective use of data, analytics, and AI, CEOs need to develop their own core skill sets around data analytics while at the same time adapting, enhancing, and training their teams. They should also focus on acquiring talent pool rich in data analytics expertise and ensure the right teams surround them. Additionally, future CEOs will need to consider data to make decisions that influence every aspect of their businesses. In some cases, there will be a requirement to procure a Chief Data Scientist and a team of statisticians. Comprehensively, a data-driven CEO of tomorrow will leverage big data and analytics technologies to drive outcomes with precision and relevance across the entire business.
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Top Tips For Citizen Data Scientists To Become Experts In Big Data
Here’s how citizen data scientists can become well versed in big data
With data scientists regularly topping the charts as one of the most in-demand roles globally, many organizations are increasingly turning to non-traditional employees to help make sense of their most valuable asset: data. These so-called citizen data scientists, typically self-taught specialists in any given field with a penchant for analysis, are likewise becoming champions for important projects with business-defining impact. They’re often leading the charge when it comes to the global adoption of machine learning (ML) and artificial intelligence (AI), for example, and can arm senior leaders with the intelligence needed to navigate business disruption. Chances are you’ve seen several articles from industry luminaries and analysts talking about how important these roles are for the future. But seemingly every opinion piece overlooks the most crucial challenge facing citizen data scientists today: collecting better data. The most pressing concern is not about tooling or using R or Python2 but, instead, something more foundational. By neglecting to address data collection and preparation, many citizen data scientists do not have the most basic building blocks needed to accomplish their goals. And without better data, it becomes much more challenging to turn potentially great ideas into tangible business outcomes in a simple, repeatable, and cost-efficient way. When it comes to how machine learning models are operationalized (or not), otherwise known as the path to deployment, we see the same three patterns crop up repeatedly. Often, success is determined by the quality of the data collected and how difficult it is to set up and maintain these models. The first category occurs in data-savvy companies where the business identifies a machine learning requirement. A team of engineers and data scientists is assembled to get started, and these teams spend extraordinary amounts of time building data pipelines, creating training data sets, moving and transforming data, building models, and eventually deploying the model into production. This process typically takes six to 12 months. It is expensive to operationalize, fragile to maintain, and difficult to evolve. The second category is where a citizen data scientist creates a prototype ML model. This model is often the result of a moment of inspiration, insight, or even an intuitive hunch. The model shows some encouraging results, and it is proposed to the business. The problem is that to get this prototype model into production requires all the painful steps highlighted in the first category. Unless the model shows something extraordinary, it is put on a backlog and is rarely seen again. The last, and perhaps the most demoralizing category of all, are those ideas that never even get explored because of roadblocks that make it difficult, if not impossible, to operationalize. This category has all sorts of nuances, some of which are not at all obvious. For example, consider the data scientist who wants features in their model that reflect certain behaviors of visitors on their website or mobile application. But of course, IT has other priorities, so unless the citizen data scientist can persuade the IT department that their project should rise to the top of their list, it’s not uncommon for such projects to face months of delays — assuming IT is willing to make the change in the first place. With that in mind, technology that lowers the bar for experimentation increases accessibility (with appropriate guardrails) and ultimately, democratizes data science is worth consideration. And companies should do everything they can to remove roadblocks that prevent data scientists from creating data models in a time-efficient and scalable way, including adopting CDPs to streamline data collection and storage. But it’s up to the chief information officers and those tasked with implementing CDPs to ensure that the technology meets expectations. Otherwise, data scientists (citizen or otherwise) may continue to lack the building blocks they need to be effective. First and foremost, in these considerations, data collection needs to be automated and tagless. Because understanding visitor behaviors via tagging is effectively coding in disguise. Citizen data scientist experimentation is severely hampered when IT has to get involved in code changes to data layers. And while IT can and should be involved from a governance perspective, the key is that citizens data scientists must have automated collection systems in place that are both flexible and scalable. Second, identity is the glue in which data scientists can piece together disparate information streams for organizations to find true value. Thankfully, organizations have a myriad of identifiers about their customers to reference, including email addresses, usernames, and account numbers. And identity graphs can help organizations create order from the chaos so that it becomes possible to identify visitors in real-time, making these features essential for analyzing user behavior across devices.
Will Amazon Become The Source For Home Repair?
Amazon has just launched a marketplace to offer consumers a place to search for professionals called “Amazon Home Services.” Offering 700 separate options for search parameters, Amazon wants to corner the market on service professionals like home repair and desk assembly. It intends on offering these services to Amazon customers, so long as they happen to reside in one of the selected states. The good news is that the roll out is pretty wide in the US and there is a lot of value to be found.
Amazon will directly compete with Angie’s List, Yelp, Craigslist and other local directories or marketplaces. However the main different of Amazon Home Services will be that consumers can actually purchase the service right on Amazon!
The Craigslist FactorThat idea, that local communities can use the web to change buying habits, has only grown in popularity since then.
There are a few competitors on the market. Yelp has had some questionable publicity that has degraded its credibility a bit, but Angie’s List has become popular. Amazon will face established competition in this space, including trendsetters.
DeploymentAmazon will offer its Home Services feature to 40 states during the initial roll out. New York, Seattle and other major metro areas like Los Angeles and San Francisco will be included in that rollout. No word yet on where the service is headed in the future, or which cities are likely to see it next, so you can consider this something of a beta test for what’s to come.
For now, Amazon is restricting access on the professional side to an invite-only basis. Any professionals who want to join the service will need a background check, insurance to cover job-related mishaps and can still only enroll through the invite process.
The fact is that this move makes sense for Amazon, which has risen to become a giant among retailers, and a thorn in their side. Inexpensive goods and fast shipping speeds have made Amazon competitive and revolutionized retail. With so many consumers ordering products that would require professional services, it made sense for Amazon to offer a resource for professional contractors.
How it WorksAmazon will offer reviews for services on its site without a membership requirement. Anyone is free to read reviews or even use a service. Services list estimates to help customers gauge what they offer, but no subscription is required to read reviews, and transactions take place on the site. Several services list estimated prices.
What separates the Amazon approach from the run-of-the-mill Angie’s List approach is that the customer can place an order for a product and its service at the exact same time. Angie’s List offers service provider information, Amazon offers the entire package.
Amazon also has a “Happiness Guarantee” that states how it handles a customer dispute. Its customer service is widely known to be top-notch, so there will be significant pressure on service providers to perform well.
A New Layer to Local?Providers have to ask themselves one question: how are they going to get in on this business? Amazon is a massive company, which has some revenue problems but has shown tremendous growth. Consumers have come to expect that value. Will this market of local providers spawn a new micro-economy of job seekers and handymen, or will we see the same major players on Angie’s List and Yelp dominating Amazon too?
There is also the weight that an Amazon page will carry. Google is quick to provide Amazon and other retail stores as its top results when the query is reflective of a purchase, will the same be true now that there are services on offer? If so, that’s a huge competitive boost for Amazon sellers who work with these providers or work with this function.
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How To Become A Financial Data Scientist?
It is quite impossible to think of a field that doesn’t rely on data science. Out of all the industries, finance sector seems to be the one where we can find heavy usage of data science. Being the backbone of the world’s economy, the financial industry has long back understood the importance of data for making informed profitable decisions. In the finance industry, the need to transform data to detect frauds, to establish how the stock market works and most importantly to improve the experience of the customers has always seen an upward trajectory. This is exactly why a financial data scientist is always high in demand. The major areas that a financial data scientist looks into include fraud detection, consumer analytics, risk management and customer experience among others. If you have been aspiring all this while to become a data science expert in the field of finance but are not sure as to how to proceed then you are at the right place. In this article, we will talk about how to become a financial data scientist and what are the pre-requisites of the same. Keep reading!
How to become a financial data scientist?Since the very core of a
Data analysisSince the job role demands the professional to work on data, it is quite evident that he/she is familiar with data analysis and its techniques. Here, everything from statistics, decision sciences, operations research, and econometrics to predictive analytics is taken into account. A financial data scientist should not only be able to define the data analysis problem but also judge how good the quality of data is, make the right assumptions wherever required, make use of the right statistical models to work on the data, perform data analysis using the required technical tools, infer the results of the analysis correctly and lastly present the data in a meaningful format to the stakeholders. Simply put, sound knowledge of data analysis is the key for a successful financial data scientist.
Should be technically soundIt is quite obvious that the data you’d be dealing with would be humungous. Thus, the manual analysis wouldn’t serve any purpose. It is here that technical tools come into play. It is important to realise that in addition to data analysis, one must be able to use a set of tools and programming languages to excel at what they are doing. On that note, Python, R, SQL, NoSQL, etc. are the most common tools/languages that come in handy for a financial data scientist. Also, as there is no limit to the amount of knowledge one wishes to gain, you can always go a step further to try your hands on frameworks such as Hadoop, Mapreduce, Spark and machine learning.
Data wrangling Knowledge of key systems used in the finance industryAs a financial data scientist, it is expected that you have significant knowledge of key systems used in the finance industry such as SAP, SWIFT, Oracle, etc.
It is quite impossible to think of a field that doesn’t rely on data science. Out of all the industries, finance sector seems to be the one where we can find heavy usage of data science. Being the backbone of the world’s economy, the financial industry has long back understood the importance of data for making informed profitable decisions. In the finance industry, the need to transform data to detect frauds, to establish how the stock market works and most importantly to improve the experience of the customers has always seen an upward trajectory. This is exactly why a financial data scientist is always high in demand. The major areas that a financial data scientist looks into include fraud detection, consumer analytics, risk management and customer experience among others. If you have been aspiring all this while to become a data science expert in the field of finance but are not sure as to how to proceed then you are at the right place. In this article, we will talk about how to become a financial data scientist and what are the pre-requisites of the same. Keep reading!Since the very core of a financial data scientist is a blend of finance and data, you are expected to possess certain skills that showcase the same. Usually, skills in the below mentioned areas are desired to land up a role as a financial data scientist –Since the job role demands the professional to work on data, it is quite evident that he/she is familiar with data analysis and its techniques. Here, everything from statistics, decision sciences, operations research, and econometrics to predictive analytics is taken into account. A financial data scientist should not only be able to define the data analysis problem but also judge how good the quality of data is, make the right assumptions wherever required, make use of the right statistical models to work on the data, perform data analysis using the required technical tools, infer the results of the analysis correctly and lastly present the data in a meaningful format to the stakeholders. Simply put, sound knowledge of data analysis is the key for a successful financial data chúng tôi is quite obvious that the data you’d be dealing with would be humungous. Thus, the manual analysis wouldn’t serve any purpose. It is here that technical tools come into play. It is important to realise that in addition to data analysis, one must be able to use a set of tools and programming languages to excel at what they are doing. On that note, Python, R, SQL, NoSQL, etc. are the most common tools/languages that come in handy for a financial data scientist. Also, as there is no limit to the amount of knowledge one wishes to gain, you can always go a step further to try your hands on frameworks such as Hadoop, Mapreduce, Spark and machine learning. Data wrangling (the process of converting raw data into a meaningful form) is one of the most crucial tasks of data science. Yes, tools and technologies do help here. But a mind that is able to absorb and form relationships between various data sources and combine them efficiently in an accurate and meaningful way makes it a lot chúng tôi a financial data scientist, it is expected that you have significant knowledge of key systems used in the finance industry such as SAP, SWIFT, Oracle, etc. The above-mentioned skills play a pivotal role when it comes to becoming a successful financial data scientist. You don’t need a degree to justify that you are knowledgeable enough. These skills can be acquired and polished using other mediums as well such as online courses, boot camps, reading books, etc. Ultimately, what everything boils down to is what you bring to the table and what role do you play in enabling the business to achieve its goals.
5 Striking Pandas Tips And Tricks For Analysts And Data Scientists
Overview
Pandas provide tools and techniques to make data analysis easier in Python
We’ll discuss tips and tricks that will help you become a better and efficient analyst
IntroductionEfficiency has become a key ingredient for the timely completion of work. One is not expected to spend more than a reasonable amount of time to get things done. Especially when the task involves basic coding. One such area where data scientists are expected to be the fastest is when using the Pandas library in Python.
Pandas is an open-source package. It helps to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data.
If you’re new to Pandas then go ahead and enroll in this free course. It will guide you through all the in’s and out’s of this wonderful Python library. And set you up for your data analysis journey. This is the sixth part of my Data Science hacks, tips, and tricks series. I highly recommend going through the previous articles to become a more efficient data scientist or analyst.
I have also converted my learning into a free course that you can check out:
Also, if you have your own Data Science hacks, tips, and tricks, you can share it with the open community on this GitHub repository: Data Science hacks, tips and tricks on GitHub.
Table of Contents
Pandas Hack #1 – Conditional Selection of Rows
Pandas Hack #2 – Binning of data
Pandas Hack #3 – Grouping Data
Pandas Hack #4 – Pandas mapping
Pandas Hack #5 – Conditional Formatting Pandas DataFrame
Pandas Hack #1 – Conditional Selection of RowsTo begin with, data exploration is an integral step in finding out the properties of a dataset. Pandas provide a quick and easy way to perform all sorts of analysis. One such important analysis is the conditional selection of rows or filtering of data.
The conditional selection of rows can be based on a single condition or multiple conditions in a single statement separated by logical operators.
For example, I’m taking up a dataset on loan prediction. You can check out the dataset here.
We are going to select the rows of customers who haven’t graduated and have an income of less than 5400. Let us see how do we perform it.
Note: Remember to put each of the conditions inside the parenthesis. Else you’ll set yourself up for an error.
Try this code out in the live coding window below.
Pandas Hack #2 – Binning of dataThe data can be of 2 types – Continuous and categorical depending on the requirement of our analysis. Sometimes we do not require the exact value present in our continuous variable. But the group it belongs to. This is where Binning comes into play.
For instance, you have a continuous variable in your data – age. But you require an age group for your analysis such as – child, teenager, adult, senior citizen. Indeed, Binning is perfect to solve our problem here.
To perform binning, we use the cut() function. This useful for going from a continuous variable to a categorical variable.
Let us check out the video to get a better idea!
Pandas Hack #3 – Grouping DataThis operation is frequently performed in the daily lives of data scientists and analysts. Pandas provide an essential function to perform grouping of data which is Groupby.
The Groupby operation involves the splitting of an object based on certain conditions, applying a function, and then combining the results.
Let us again take the loan prediction dataset, say I want to look at the average loan amount given to the people from different property areas such as Rural, Semiurban, and Urban. Take a moment to understand this problem statement and think about how can you solve it.
Well, pandas groupby can solve this problem very efficiently. Firstly we split the data according to the property area. Secondly, we apply the mean() function to each of the categories. Finally we combine it all together and print it as a new dataframe.
Pandas Hack #4 – Pandas mappingThis is yet another important operation that provides high flexibility and practical applications.
Pandas map() is used for mapping each value in a series to some other value-based according to an input correspondence. In fact, this input may be a Series, Dictionary, or even a function.
Note – Map is defined on Series only.
Pandas Hack #5 – Conditional Formatting Pandas DataFrameThis is one of my favorite Pandas Hacks. This hack provides me with the power to pinpoint the data visually which follows a certain condition.
You can use the Pandas style property to apply conditional formatting to your data frame. In fact, Conditional Formatting is the operation in which you apply visual styling to the dataframe based on some condition.
While Pandas provides an abundant number of operations, I’m going to show you a simple one here. For example, we have the sales data corresponding to each of the respective salespeople. I want to highlight the sales values as green that is higher than 80.
Note – We have applied the apply map function here since we want to apply our style function elementwise.
End NotesTo summarize, in this article, we covered seven useful Pandas hacks, tips, and tricks across various pandas modules and functions. I hope these hacks will help you with day-to-day niche tasks and save you a lot of time. In case you are completely new to python, I highly recommend this free course-
Related
Must For Data Scientists & Analysts: Brain Training For Analytical Thinking
Introduction
Let’s start this article with a small exercise. Take a pen and paper and write the answer as it comes to your mind. No thinking twice and you shouldn’t take more than 15 seconds to do it.
On this paper, please write the answer to “What are the skills required to become a successful data scientist?”
A lot of you would have written coding, knowledge of analytics tools, statistics etc. All of these are definitely required to be a successful data scientist, but they are not sufficient.
One of the most important skill differentiating a good analyst / data scientist from the bad one is the ability to take complex problems, put a framework around it, make simplifying assumptions, analyze the problem and then come up with solutions. And analytics tools are just a medium to do so.
In today’s article we will take a case study and see this process of problem solving in structured manner.
What you’ll learn ?Here you’ll find practice problems to train your brain think analytically while solving complex problems. This brain training will not only introduce you to a new approach to solve problems but will also help you to think faster while dealing with numbers!
My previous article on how to train your mind for analytical thinking? should give you a good head start.
Practice ProblemHere’s is my daily routine:
I get ready and leave home for office at sharp 10:30 AM every working day. Considering the amount of work I got to finish on some days, I try to reach early by driving faster than other days (obviously in safe limits).
However, since last 5 days, I’ve observed that I reach office almost at the same time, irrespective of my average speed between traffic lights. This makes me wonder, whether the time taken from my home to office is dependent on my velocity or not? In other words, the total average velocity adjusted by the traffic lights to the same level, and does not depend on the velocity we drive the car!
Take the Test: Should I become a Data Scientist ?
To explain you better, consider a simplistic scenario:Two cars start from point A which is the first traffic signal. Point B is a traffic signal with a halt time of 60 sec and drive time of 20 sec. The distance between A and B is 600m. Car1 starts at 5m/sec and Car2 starts at 6m/sec. Who will cross the traffic light first? Here are the assumptions:
1. Traffic lights are configured for average speeds, it becomes green 120 seconds (600 m / 5 m/sec) after the first signal turns green.
2. Traffic lights are green for 20 seconds and red for 60 seconds (20 * 3)
Assume both cars start at 0 sec.
Time taken for Car1 to reaches signal B = 600/6 = 100sec Time taken for Car2 to reaches signal B = 600/5 = 120sec Light is green at (40,60) ; (120,140) ; (200,220) ; (280,300)Hence, cars reaching point B in 61 sec and one reaching at 140 second show no difference in terms of passing through the second signal. Let’s calculate the min and max speeds which will show no difference amongst the two lights scenario :
Minimum speed = 600m / 120sec = 5 m/sec = 18 km/hr Minimum speed = 600m / 61sec = 9.8m/sec= 35 km/hrIt does not matter whether you drive at 18 km/hr or 35 km/hr in this scenario, you will cross the second signal (B) at the same time. In general, it is difficult to drive in such wide range of speeds in peak time traffic and hence my concerns looks logical now. I probably have no control on the time I will take to reach office (obviously this is over simplification of the problem).
Let’s make it more complex…!Now we have 4 signals A,B,C and D. Same two cars start from A at the time 0 sec. Distances between AB , BC and CD are same. The question is now, who will cross the signal D first.
Without going into mathematics, the answer is very straight forward. If both will cross B at the same time, A – B pair is the same as B-C pair which is in turn same as C-D pair. Hence both the car will cross D at the same time. The scenario is actually more extreme, the car which maintains an average speed of 18 km/hr and the one at 35 km/hr will cross D at the same time. This further strengthens my hypothesis.
Question again boils down to :
“Am I just a helpless puppet in traffic police’s hand while driving to my office ? “
Let’s try to generalize it into a parametric equationActual scenario is too difficult to generalize in this article, so let’s ground a few assumptions :
1. Traffic lights turn green for time t sec and becomes red for time 3t sec
2. Average speed of a vehicle on road is v m/sec
3. The challenger to the average vehicle drives at a velocity x times v m/sec
By now, we already know, it hardly matters if we solve for one pair of traffic light or more. If the faster driver is able to sneak through the traffic light in a green signal before the average vehicle, it will make a difference or else not.
Hence, the difference in time required to make this happen will be 3t. Following is the final equation we are solving for :
Time taken by average vehicle : l/v sec
Time taken by faster vehicle : l/vx sec
It simplifies to ;
Given x , v, l and t are all positive, this can be further simplified to :
Here is a JACKPOT! We know that l is always positive, hence to make the above equation practical, both x and (l – 3tv) have to be positive. This means if 3tv becomes more than l, you have no chance of beating traffic lights. For instance, if t = 30 sec, v = 5 m/sec and l = 145 m, you simply cannot beat the odds, even if you ride on speed of GUN shot!
Let’s assume a few parameters and understand the equation further:Say, l = 600 m. The equation becomes :
So, here are a few thumb rules to make it possible to beat the Traffic signals :
1. Minimize t (cycle of traffic light) : It is possible to beat traffic light in quick traffic localities where it turns Green – Red in quick time.
2. Minimize v (the average velocity of the road ) : If the average velocity on road is exceptionally low, we can beat these slow drivers if we drive fast (Duh!)
3. Maximize x (Faster multiplier) : If we drive super fast, we can still win the race. But notice if v*t becomes more than 200, you have no chance of getting
Don’t miss: Introducing the art of structured thinking and analyzing
Let’s try to visualize a few relationsAverage t in Bangalore is about 20 seconds and average speed is 5m/sec. Hence the equation becomes :
As seen from the above graph, if x and l are high enough to fall into the shaded region, we have a chance to beat the traffic light.
Let’s summarize our findings1. There is no point of driving fast on a lane where 3 * Green light time * average velocity is more than the length of the road.
2. Beating traffic is possible if following are in our favor :
a. High x . We drive really fast (not a safe option)
b. High l. For instance driving fast on a highway makes sense
c. Low t : No point of driving fast on a high timer traffic signals road
d. Low v : If the average velocity on the road is really low, we can beat them. We already knew that!
End NotesI hope, you enjoyed solving this traffic problem. I’m sure it would have challenged your thinking which was our motive. Right ?
In this article, using a case of traffic light and some elementary physics concepts I have explained the necessary skill required to build a unshakable foundation to become a data scientist.
Did you enjoy reading this article? Have you wondered over this question before? Do you think you can improvise these calculations further to make it more realistic?
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