The career mentoring sessions helped me a lot in preparing for the interviews: Soumya, PGP-DSE Alumnus

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Where there is a will, there is a way. Read on to find out as Soumya, our PGP-DSE alumnus, tells us how being focussed and persistent helped her to transition into a data-oriented role, given her non-technical background.

Why did you choose Great Learning to learn Data Science?

I completed my graduation in BSc maths, economics and statistics from Christ University, Bangalore. After graduation, I took up the PGP-DSE program. I opted for this program because Great Learning has a good brand reputation and the duration of the program is only 6 months. I was always interested in maths and statistics and that’s how I developed an interest in data science, even though I am from a non-programming background. I didn’t even consider any other options before enrolling for Great Learning’s PGP-DSE program.

What did you like the most about the program?

There was a lot of hands-on learning and for someone like me who is completely new to programming, it was a kick start to learn all the new tools, especially python. It was easy for me to adapt because a lot of hands-on sessions used to happen.

How did the curriculum help you prepare for data science?

A lot of overviews were given about the field at the beginning of the program. For me, it was a very useful introduction. I became familiar with all the aspects of data science and got an idea of the various machine learning algorithms. When you have an idea of everything, you can figure out what you are good at and what can you improve on. Being averse to computer science, it was challenging for me to sit and do programming but I learnt after a fair amount of practice.

How was your experience with Great Learning’s career support?

I think we had good support from the Great Learning team in terms of placement grooming and career support. We had multiple CV reviews and mock interviews as well. Career mentoring sessions helped me a lot in terms of preparing for the interviews. Mr Srijit Ghatak, who was my career mentor, really helped me to prepare for the Mu Sigma process. He guided me on guesstimate questions, aptitude, logical reasoning, verbal ability, business approach etc. I was a bit disappointed when Mu Sigma was the only company to have shortlisted my profile, apart from Monexo and Saksoft during the career fair at Chennai. They had conducted aptitude test, group discussion followed by face to face interview. The course did, of course, help me in group discussion and the interview. My overall experience with Great Learning has been really good.

Do you have any advice for the aspirants?

  1. If you are not from an engineering background, I think you should be emotionally strong as you will face a lot of setbacks in the placements. You are more like an outlier compared to others and you need to be prepared for that. Also, you will have to put more efforts than others.
  2. Concentrate and practice more to become comfortable with programming.

I’d like to thank the Great Learning Mentors for their guidance and support: Aswathy, PGP-DSE Alumnus

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There’s a higher chance of career success when you get guidance from industry experts, who can direct you in a path that’s right for you. The PGP-DSE program is successful in a large part due to the exceptional career and learning guidance offered by the experienced industry mentors. Here’s Ashwathy’s learning journey, in her own words:

I am glad to inform you that I have been offered the position of ‘Decision Scientist’ in Mu Sigma. I would like to thank the Great Learning Mentors for their guidance and support.

To tell you more about the Mu Sigma process, Mr Ankur’s session for the aptitude, reasoning and video synthesis provided the platform for what to expect in the initial stages of the interview.

Special thanks to Mr Tomy for his interactive sessions on how to crack the Mu Sigma interview. Starting from the nuances of the step-by-step approach to be followed in the group activity to the possible challenges we could face in the personal interview, his sessions proved to be extremely helpful. I could relate to and follow his advice on the do’s and don’ts during the interview process, which helped me land this job. He was readily available to answer my queries and concerns throughout. He extended his support and followed-up until I got the offer letter which I regard highly.

Coming to the PGP-DSE course, Mrs Uma and Mr Vinodh were ready to take feedback from us, hear out our concerns and doubts, and come up with suitable action plans like arranging additional classes. This was extremely helpful in getting clarity on the topics covered.

I would like to thank all the professors of the PGP-DSE course who imparted their knowledge to us on various subjects. Special Thanks to Mr Mahesh Anand for his great sessions.

I would also like to thank Ms Richa Agarwal who was my mentor during the Excelerate session. Her tips to improve my CV and also on preparing for the analytics interviews were extremely helpful. Great Learning’s Excelerate is a great idea which connected us with the experienced industry professionals and we got to know their views on the data science field.

I thank our Project Group Mentor, Mr PV Subramaniam who extended his guidance. We had the opportunity to do more hands-on learning during the project phase and learn from his expertise. The importance of the Capstone Project was visible in the Interview, where in-depth knowledge was tested.

Again, thanks to the management and team for the efforts they had taken, I highly value it!

Great Learning has helped me in making this transition with ~45% hike: Koyeli, PGP-BABI Alum

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When decisions are driven by passion, excuses take the backseat. When Koyeli, our BABI alum decided to learn analytics, nothing could stop her. Read on to find out how she managed to learn analytics even though she had a full-time job.

Why did you take up the PGP-BABI program?

I have completed my BTech and I have worked with Ericsson for 5 years. There I was majorly doing work related to Network Engineering in the telecom domain. I was also working on a lot of automation opportunities in my own team. Then I joined Reliance Jio, there also I started working in the RF department- Network side. My job role was optimization and Network KPI Visualization using R. I was doing a lot of analysis on volumes of data and wanted to learn more about deploying analytics in the job. That is the main reason for taking up the PGP-BABI program. After completing the program, I was able to implement various concepts that I learnt during the class in my day-to-day work.

How did the program help you transition to your current role?

I got an interview call from Renault through one of the job postings of Great Learning. The first interview was entirely based on the basics of data science which was followed by a technical round. Most of the questions were on probability, statistics, SQL and python. After that, I had two face-to-face interviews. Mr Vijayakeerthi who is a Great Learning alumnus helped me in identifying the areas to focus on, for the interview. Renault offered me the role of senior ML engineer. I will be using Python, Spark and SQL in this role dominantly.

Did the program give you the flexibility to establish a work-life balance?

Initially, it was a bit difficult to manage with a full-time job. I have sacrificed weekends for one year even though the classrooms were held only on one weekend per month. The assignments and quizzes kept me occupied. Candidates need to give their best and practice more once you enrol for such a program. The best thing about the PGP-BABI program is that the faculties are really good.

The learning outcomes of the program have definitely helped me as this is a totally different domain and everything I learnt through the assignments and projects has really helped. The course is really well defined. Also, the resume building sessions and guidance from Great Learning has helped me in making this transition with approximately 45% hike.

How Artificial Intelligence AI can Help the Physically Challenged

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The world population is over 7 billion and over 15% of the population is physically challenged in one form or another. According to surveys, only one in ten people with a form of disability have access to any form of assistive technologies or products. Braille for the visually impaired, hearing aid for the hard of hearing, wheelchairs for a physical malady, are among the most common forms of aid that human intelligence has come with up. Technology has helped mankind in a myriad of ways.

Now, let’s talk about a few ways AI can help the specially-abled. Advances in AI like Speech-to-Text transcription, predictive text, and facial recognition promise a more inclusive future for all of humanity.

AI is Bridging the gap for the visually impaired.

Efforts are being taken to create a more accessible environment for the visually impaired. Text-to-speech can help to describe emoji from pictures. This could possibly serve as a digital eye, thanks to various AI techniques.

It is also interesting to see how the visually impaired can profit by self-driving cars. At 100 percent efficiency, self-driving cars can enable hassle-free transport for all, regardless of their driving ability.

AI for Accessibility’, which is a five-year program by Microsoft, with an investment of $25 million, promises to put AI in the hands of developers to make the world more accessible by providing AI solutions for the specially-abled.

“AI can be a game changer for people with disabilities. Already we’re witnessing this as people with disabilities expand their use of computers to hear, see and reason with impressive accuracy,” -Brad Smith, President and CLO at Microsoft.

Microsoft is also assisting the hearing impaired with real-time captioning for conversations. The students of Amity International School in Gurugram developed an app called ‘Practikality’ which is a machine learning based assistant which helps the differently-abled communicate efficiently.

Ellie Southwood, who has sight loss says the Amazon Echo dot makes her feel more included. “I spend far less time searching for things online; I can multi-task while online and be more productive. Microsoft’s Seeing AI app means I can recognize people and scenarios and make up my own mind about what’s going on.”

AI is powerful enough to foster inclusion 

Advances in artificial intelligence promise a more inclusive environment for the masses. Biometric attendance systems make it easier for people with dyslexia who find it difficult to remember passwords login easily. Predictive text, text-to-speech, speech-to-text, have already showcased promising results when it comes to helping people with vision and hearing impairment.

Another application where Artificial Intelligence is making its presence felt is Exoskeletons. Technology is drastically changing for the physically challenged as well. Robotic exoskeletons have made it easy for people with physical deformities to walk around. Although the technology is still improving, the possibilities are infinite.

GnoSys is a smartphone application explicitly developed for deaf and mute people. Also called the ‘Google translator for the deaf and mute’, the app uses natural language processing, neural networks, and computer vision to translate gestures and sign language to speech. According to the National Deaf Association of India, 18 million people are hearing impaired or are hard of hearing. The app is expected to hit the Indian market in 2019 and promises to change the lives of 18 million people in India alone. Roman Wyhowski Founder & CEO Evalk believes this app is the need of the hour as most of the existing translation software in the market are slow and expensive. Showcased in Netherlands recently, GnoSys can translate as fast as a person speaks, translate sign languages to text and speech, and can be plugged into assistants as well.

Whether it is to assist or to empower them, Artificial Intelligence technologies will leave no stone unturned to create an impact in the lives of the specially-abled, both physically and mentally.

9 Interesting Applications of Data Science in the E-commerce industry

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There is no doubt that as of today, companies all over the world, big or small, are incorporating data science and its applications within their business in one way or another.

The importance of data in today’s world has reached new heights so much so that companies are taking business decisions only after a thorough analysis of relevant data.

This has especially found an important place in the e-commerce and retail industry. They can predict the purchases, profits, losses and even nudge customers into buying additional products on the basis of their behaviour. Organizations also use purchase data to create psychological portraits of a customer to market products to them and use it to drive customer loyalty and thereby more revenues.

Here are 9 interesting applications of how data science is used in the e-commerce industry

1)  Recommendation engines

Recommendation engines are the most important tools in a retailers arsenal. Retailers leverage these engines to drive a customer towards buying the product. Providing recommendations helps retailers increase sales and to dictate trends.

Sound familiar? Thinking of Amazon and Netflix? That’s exactly how search recommendations work.

How do they do this?

Well that’s simple, the engines are made up of complex machine learning and deep learning algorithms and are designed in such a way so that they can keep a track record of the individual behaviour of customers, analyse their consumption patterns and give them suggestions based on this data.

That’s why everytime Netflix recommends a movie or TV series to you, it’s probably something you are going to watch!

The same thing works with Amazon too, based on your past searches and purchase history, amazon provides recommendations and discounts on them as well. Because let’s face it, who can resist buying something that they always wanted to especially when it comes with a discount.

This process is very complex and involves a great deal of data filtering and reading and all this passes through the machine learning algorithm.

2) Market Basket Analysis

This is one the most traditional tools of data analytics, retailers have been profiting off it for years.

Market basket Analysis works on the concept- if a customer buys one group of items, they are more or less likely to buy another set of related items. For example, if you went to a restaurant and ordered starters or appetizers without any drinks, then you are more likely to order main course or desserts. The set of items the customer purchases are known as an itemset, the conditional probability that a customer will order main course after starters is known as the confidence.

In retail, customers purchase items based on impulse, and market basket analysis works on this principle by predicting what the best chances of a customer making a purchase are and for what item.

This mostly involves a lot of how the marketing of the product is done by the retailers, and in the world of e-commerce, customers data is the best place to look for potential buying impulses. Similar to search recommendations, market basket analysis also works with a machine learning or deep learning algorithm.

3) Warranty Analytics

Warranty data analytics helps retailers and manufacturers keep a check on their products, the potential lifetime of their products, problems, returns and even to keep a check on any fraudulent activity. Warranty data analysis depends upon the estimation of failure distribution based on data including the age and number of returns and the age and number of surviving units in the field.

Retailers and manufacturers keep a check upon how many units have been sold and among them how many have returned due to issues after analyzing the data. They also concentrate on detecting anomalies in warranty claims. This is an excellent way for retailers to turn warranty challenges into actionable insights and price their warranties and offer it as a bundle to customers with their purchase of their goods.

4) Price Optimization

Selling a product at the right price, not just or the customer but also for the retailer or manufacturer is an important task. The price must not only include the costs to make the product but also should also take into account the ability of a customer to pay for that product while keeping in mind competitor prices as well in order to drive profits.

All of this is calculated with the help of machine learning algorithms again, the algorithm analyzes a number of parameters from the data like the flexibility of prices,  location of the customer, the buying attitude of an individual customer and competitor pricing.

It then comes up with the optimal price that can benefit all the parties.

This another powerful and important tool for retailers to market their product in the right way with the optimal pricing that aligns with the company’s business goals.

5) Inventory Management

Inventory refers to the stocks of goods an organization stores in order to ensure a streamlined supply chain that can continuously cater to customer demand on a regular basis. Inventory management is key because a organization/retailer has invested money in purchasing stocks and that capital is lying idle till it is sold. The retailers should be able to stock the right goods in the right quantities in order to provide it to the customer when there is demand of that product. In order to achieve this, the stock and supply chains are analyzed thoroughly.

Powerful machine learning algorithms analyze the data between the elements and supply in great detail and detect patterns and correlations among purchases. The analyst then analyzes this data and comes with a strategy to increase sales, confirm timely delivery and manage the inventory stock.

6) Location of new stores

Location analysis is an important part of data analytics. Before a business can decide where to open up their stores, they do a ton of analysis and figure what the best location to set up shop would be.

The algorithm used in this case is simple, yet effective. The analyst analyzes the data giving importance to demographics. An analysis of zip codes and demographic information gives a basis for understanding the potential of the market. And competitor markets are also taken into consideration. Additionally, a retailers network analysis is performed. The algorithm gives the best possible output after taking into consideration all of these points.

7) Customer sentiment analysis

Customer sentiment analysis has always been around the business world for a long time. But now, machine learning algorithms help simplify, automate and saving a lot of time while giving accurate results.

Social media is the most readily and easily available tool for an analyst to perform customer sentiment analysis. He uses language processing to identify words bearing a negative or positive attitude of the customer towards the brand. This feedback helps the business improve their product and services to meet consumer needs.

8) Merchandising

Merchandising is an essential part of any retail business. The idea is to come up with strategies that increase sales and promotions of the product.

Merchandising helps influence customer decision making via visual channels. Rotating merchandise helps to keep an assortment always fresh and new. Attractive packaging and branding helps get customers attention.

The merchandising algorithms go through the data picking up insights and forming priority sets of customers taking into account seasonality, relevancy and trends.

9) Lifetime value prediction

Customer lifetime value is the total value of the customers profit to the company over the entire customer-business relationship. By taking into direct customer purchases, two significant customer methodologies of lifetime prediction are made; historical and predictive.

All the forecasts are made on the past data leading up to the most recent transactions. Usually, the algorithms collect, classify and clean the data concerning customer preferences, spends, recent purchases and behaviour as the input. After the data is processed, a linear presentation of the possible value of the existing and possible customers is received. The algorithm also spots interdependencies between the customers characteristics and their choices.



Data science has applications across all sectors of technology, it helps businesses make better decisions based on data,. The 9 applications listed above are among the most popular and important ones in the e-commerce field.

5 ways to check if Data Science is the best career option for you

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It’s very easy to get caught up in the latest trends -whether it’s around fashion, the latest TV show or movie or even when it comes to jobs. Being a “Data Scientist” has been declared as the ‘sexiest job of the 21st century’ – and it’s very easy to want to take up a career in Data Science. But how do you know if this is the right career option for you? Given time and effort, becoming a Data Scientist is possible. But how do you know if this is the career that will interest you the most?

1.  You love working with numbers.

A data scientists job involves performing statistical analysis on a day-to-day basis. If you don’t like statistics, beware.

2. You love problem-solving

A data scientists job involves coming up with insights to help make business decisions that impact key business metrics. Attention to detail is a crucial aspect of the job and it might require concentrated effort over extended periods to time to mine relevant insights.

3. You like delving into your work to find meaningful insights

As mentioned earlier, attention to detail is crucial. A data scientist continually works with statistics, data, numbers, algorithms, and mathematical logic. In a continuous review and repeat manner, you will have to analyze every single chunk of data, missing out isn’t a choice. So detail-oriented individuals are best suited for the job.

4. You’re willing to get your hands dirty to perform your job

As a data scientist, you will have an unfathomable amount of data- both structured and unstructured. Organizing the data, cleaning and standardizing the data is an inevitable part of the job.

This is crucial as algorithms need data to be useful. So you need to ask yourself if you are willing to do data preparation over and over again till you get the right data to mine insights from.

5. You are patient while expecting results from data

Now we know that this data-driven job is all about detail. The job also involves a lot of trial and error until you get the right results. You will need a tremendous amount of patience to continuously work on your data-sets till you get the right result. Instant gratification isn’t usually a part of the job description.

Once again, It’s all about data!

“You can have data without information, but you cannot have information without data.”

With piles of data at your reach, you need to clean, organize, process, and analyze the data repeatedly. So let me ask you- Do you love working with data or not?

It is not just about having a degree in data science. As a data scientist, customers will put forward their problems to you. You will be expected to understand the business first, and then the root cause of the problem. A data scientists role is very impactful to a business. Business decisions taken on accurate insights can lead to tremendous growth for the organization and ultimately help your career. If you’re looking to work in an interesting field that is also very critical to your organization, then a career in Data Science is the right step for you!

How Will Artificial Intelligence Create More Jobs by 2025?

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Any new technology that has the potential to change the way humanity lives has always created a huge amount of debate. And this is especially true for Artificial Intelligence. The debate over AI is never-ending. Researchers, thinkers, IT professionals, even the average layman has polarizing opinions on AI and its potential impact on humanity.

“I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” —Claude Shannon.

“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” —Gray Scott.

“Sooner or later, the U.S. will face mounting job losses due to advances in automation, artificial intelligence, and robotics.” —Oren Etzioni.

“I believe this artificial intelligence is going to be our partner. If we misuse it, it will be a risk. If we use it right, it can be our partner.”

The list goes on and on. But a recent study seems to provide a conclusion to this debate.

So Here’s Why AI Will NOT Destroy Jobs.

Automation threatens 8 million jobs by the end of 2030 but at the same time technology is all set to create more jobs than ever. Artificial intelligence is not purely destructive. New jobs will be created, existing roles will be re-built, and switching careers will be a great opportunity, says a report.

Reports claim that over 30% of jobs are under potential threat with some people already losing their jobs. Digging in deeper, we establish that AI technology will also create over 2 million job opportunities worldwide by the end of 2020.

While there is cut-throat competition across all jobs, machine learning and artificial intelligence jobs face substantially less competition. These specialized engineering jobs are on a rise but still remain vacant. The critical factor is the scarcity of skilled talent.

This report talks about why is it necessary for IT professionals to upskill to stay relevant.

Read the entire report here.

The AI research spectrum is expanding. From autonomous cars to models for cancer detection, the use cases are touching almost every segment. At the same time, there are a plethora of platforms offering degrees and certifications in AI, Machine Learning, and Deep Learning but the number of potential employees for the same is very less.

Presently, with very little job seekers, the hiring processes have become sloppy, delaying deft adaptation of intelligent machines.

Automation Requires Creating New Skill Sets.

While unskilled jobs are under grave threat, AI will create room for a new category of jobs which can be mastered with training.

If you do not believe in this philosophy, now is the right to start believing it. While a lot of jobs have taken a hit due to artificial intelligence, you can still land yourself a job with constant learning and upskilling.

Robots and machines are becoming smarter with artificial intelligence and are taking over time-consuming, manual labour based jobs which might be threatening but we need to address the fact that this has been an on-going process. In the early 60’s, we would reach out to the nearest branch to withdraw money but with ATMs in place, the process is fastened.

Robots for painting cars, ATMs for cash disposal, computers for creating spreadsheets, did feel like a potential threat initially. Similar could be the case with AI, we never know.

A global survey by Allegis had fascinating insights- Twenty-one percent of the people viewed AI as something to be excited about. Seventeen percent considered it both disrupting and enabling, and a lower number, 9 percent, believed AI will displace most jobs in 10 years.

“This mixed view of AI is not surprising because the technology does more than automate tasks; it changes the nature of the work we do.” -Rachel Russell.

As AI becomes more and more able to carry human-like functions it will replace jobs with certain human attributes but will create new opportunities as well.

Machine learning engineer, Deep Learning engineer, AI trainers, NLP engineer, AI specialist, Deep Learning engineer – Computer Vision followed by multiple permutations and combinations like AI & Dl, DL, and ML, DL and Data Scientist etc are among the trending job profiles to name a few.

Of all the advancements, Human judgment is less likely to be surpassed by AI. We need to start looking at Artificial intelligence in its purest form- Intelligence in augmented form instead of a job-hungry robot training to take over.

According to a report by the World Economic Forum, 50% of tasks in the workplace will be automated by machine by 2025 compared to 29% as of now. Nearly 50% of companies predict their workforce to reduce by 2022 but at the same time, automation is expected to create new roles in the industry. Another key takeaway is that the demand for roles varies across regions.

The entire hype about AI replacing jobs has a lot to do with the type of jobs under consideration. While certain aspects of a job are susceptible to automation, human intervention cannot be replaced.

AI can power multiple aspects which make up today’s jobs.

Machines are great at cyclically performing a particular task with a high level of accuracy and consistency, thus we can surely say AI will take over a particular variety of tasks.

But complex problem solving remains a far-fetched thought among the goals of AI. Change is the only constant and as some jobs are replaced new ones will be created. As per some estimates, 65% of the kids who are in schools today will end up with jobs which do not exist today.

When it comes to tactical thinking, nuances of problem-solving, adaptive thinking, and thinking-out-of-the-box abilities, AI is still way behind the human brain. After all, AI can only mimic the human brain.

So it seems AI will cut off the monotonous jobs on the table (like data entry and a certain level of accounting) but human resource-based jobs like customer care, sales and marketing, innovation, and research will continue to be in high demand along with specialized jobs in the field of AI itself.

For those of you who are wondering what should you do to save your job- a little less worry and more upskill and training will set the game just right for you.

A Beginners Guide to Data Science

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You’ve probably heard the word ‘Data Science’ pop up in numerous conversations, news articles and across different media. This article is a primer on what Data science is all about.

Data science is the future of technology and is creating millions of jobs world wide. Tech giants like Facebook, Google, IBM are spending millions of dollars in research and development in the fields of Machine Learning and Artificial Intelligence which are based on Data Science concepts. Data science jobs are one of the most sought after on websites like Linkedin, Glassdoor and Monster.

What is Data Science?

As the name suggests, Data science deals with large amount ofs data.

This vast amount of data needs to be grouped, classified and structured and used to draw useful insights to drive business growth. Doing this sounds very simple,  but it actually isn’t. In order to read the data, many tools and algorithms have to be used to visualize, structure and read the data to eventually derive insights.

Data science is used as a broader generic term these days – when people use the word Data science, they are usually referring to different fields that come under Data Science, like Data Analytics, Business Analytics, Machine Learning and Artificial Intelligence. Each field is unique in it’s own way and they all have critical applications in business.

Data science flow-chart

Data Science for Beginners

The chart above shows the different steps that are part of the of a Data Scientists workflow. The rest of the article focuses on detailing these steps.

Step 1:

Obtaining the Data

One first needs to identify what kind of data needs to be analysed. This data could be around customer buying patterns or sales forecasts or even customer behavior across different touchpoints of a business. This data needs to be exported to an excel or a csv file. The next step would be to make this data easily readable, i.e. it should be labelled and structured the right way so that it is easy to analyse.

Skills and tools required

  • *Database management : SQL
  • *Understanding the database and what it represents
  • *Retrieving raw unstructured data in the form of text, docs, photos, videos etc.
  • *Distributed storage : Hadoop, Spark, or Apache


Step 2:

Scrubbing or cleaning the data

This is an important step because before you are able to read the data, you must make sure it is in a perfectly readable state, without any mistakes, no missing values or wrong values. The data has to be consistent throughout, to ensure you can make an error free analysis.

Skills and tools required

  • *Scripting language – Python, R, SAS
  • *Data wrangling tools – Python, Pandas, R
  • *Distributed processing – Hadoop, Mapreduce/spark


Step 3:

Exploratory Data Analytics

Now that your data is clean and readable, it’s time to get to the real work – Analyzing the data. This is done by visualizing the data in various ways and identifying patterns to spot anything out of the ordinary. In order to be able to analyse the data, you must have high attention to detail to identify if anything is out of place. Additionally, you need to be able to think out of the box to identify trends and build out hypotheses. And then based on this analysis, come with solutions. This is the primary job of a Data Analyst.

Skills and tools required

  • *Python libraries – Numpy, Matplotlib, Pandas, Scipy
  • *R libraries  – GGplot2, Dplyr
  • *Inferential statistics
  • *Data visualization
  • *Experimental design


Step 4:

Modelling or Machine Learning

Machine Learning is an application of Artificial Intelligence, in which, a machine can follow commands and rules (algorithms) and come up with predictive solutions without any human supervision.

The data engineer or scientist writes down a set of instructions for the Machine Learning algorithm to follow based on the data that has to be analysed. The algorithm uses these instructions in an iterative manner to come up with the right output.

After cleaning up the data and finding out essential features through the data exploration phase, using a statistical model as a predictive tool will help you develop relatively error-free business insights enabling you to improve  your overall decision making.

Skills and tools required

  • *Machine learning – supervised, unsupervised and reinforcement machine learning
  • *Evaluation methods
  • *Machine learning libraries – Python (sci-kit learn) / R (CARET)
  • *Linear algebra and multivariate calculus


Step 5:

Interpreting or ‘data storytelling’

This is the final step, in which you uncover your findings and present it to the organization. The most important skill in this would be your ability to explain your results. Hence the term ‘storytelling’.

In order to understand how the data can affect the business or how your solution helps to provide better business solutions, you must also have a good understanding of your current organizations business and business processes.

Skills and tools required

  • *Knowledge of your business domain
  • *Data visualization tools – Tableau, GGplot, Seaborn etc.
  • *Communication – presentation skills, both verbal and written

Now that you know what skills and tools you need to know in order to become a data scientist, the next step for you is to learn all these tools and enter into the vast field yourself.

5 Crucial Data Science Skills to Learn In 2019

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India has witnessed a 400% rise in the demand for Data Science professionals and according to a recent report with over 50,000 jobs lying vacantAccording to the same report, the job market is at a situation where the number of job seekers is half of the total number of available jobs, which means that opportunities are there for the taking.

But learning data science can be challenging. A customary Google search for ‘data science courses’ displays 45,30,00,000 results. There are a glut of options, and it might be daunting to choose the right course.

To help you make an informed choice, we’ll take you through the basic skills and tools that you need to start off in Data Science. Machine learning, statistics, quantitative analysis, mathematics, and programming languages are broad areas in which you’ll need to build expertise. If we were to delve a little deeper in what exactly these skills entail, you’ll come across these tools:


The lingua franca of coding, python is widely used across a wide range of applications. This open-source language has a host of open-source libraries. It is actually easy to understand and learn and is considered as the primary language for data scientists. Employers don’t just look at a candidate who knows Python alone, you are expected to have an understanding of the standard python data science libraries– numpy, pandas, scikit-learn, and matplotlib.

Read: How Boredom Led to The Creation of Python.  


Although Python has gained massive popularity in data communities, R isn’t far behind. Most programmers are learning R for Data Science and ML applications. R as a programming language is popular in data science communities due to its robust support for statistics, clustering methods, regression techniques, and graphical methods.


Structured Query Language is considered as the primary way to interact with relational databases. When you have a relationship between large sets of  variables, SQL is predominantly used. It offers two advantages: you can access many records with one single command and it eliminates the need to specify the method to reach out for a specific record.

Learning Python, R and SQL would help you cover your bases in terms of programming languages necessary for Data Science.


This analytics and data visualization tool is easy to use and figures in many Data Scienctist job descriptions. Tableau offers a free public version, you will have to use the paid version to keep your data private.

Tableau allows us to step out of the box and look at data in a totally different way.” –Kevin King, Director, Reporting, and Analytics, Coca-Cola Bottling Company.

Hadoop and Spark

Hadoop and Spark open source tools from Apache, are used for processing large sets of data.

Apache Spark is an open-source, distributed, general-purpose, cluster-computing framework. Spark is primarily used for large-scale data processing, data engineering, and analytics.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

In addition to learning these skills and tools, here are a few pointers on how you can ace a Data Scientist interview:

Enhance your communication skills: Technical skills are very important, but you need to be able to communicate what you know well.

Have a project that you can showcase: Rather than just having a list of skills, you’d have an advantage when you can talk about a data science project that you have worked on.

These are only some of the skills and techniques you’ll need to be familiar with to be able to crack a data science interview. For a more structured and comprehensive learning approach, you can look at the Data Science Program offered by Great Learning.