Your Essential Weekly Guide to Data Science- October II

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Data science is one of the fastest evolving domains today, with professionals striving to find practical solutions for the digital economy. Dubbed as the ‘sexiest job of the 21st century’, Data science garners a surprising amount of questions. As professionals and students try to demystify the domain, we bring the latest developments and changes in it to help you stay updated. Keep reading to learn all about the top trends. 

Python 3.9: New Changes Data Scientists should Expect

Python Software Foundation released Python 3.8 on Monday, which has new features to deliver an enhanced developer experience. The changelog for Python 3.9 was introduced simultaneously, prompting data scientists worldwide to become familiarised with the changes. This article focuses on all the new advancements and modifications in the new version of the changelog. 

Synthetic data: A new frontier for data science

GDPR has made data capturing and distributing difficult for all Europe based organisations. This scarcity of data has affected businesses and data scientists alike. Synthetic data has emerged as a promising solution to this problem. Sophisticated synthetic data which is anonymously generated and is based on ghost individual can be used effectively by data scientists to replace traditional historical data.

L1ght Saves Kids From Online Toxicity, Using Data Science And AI 

Children spending more time on the internet is a growing concern for parents. L1ght has developed a platform that ensures children are protected against toxic content online. This platform uses deep learning, a subset of machine learning and data science to screen through images, texts, video, voice and sound in real-time to identify the ‘anti-toxic’ categories. The need for ‘toxicity’ moderation has pushed L1ght to come up with solutions that protect children against online abuse.

Data Scientists in MNCs Vs SMEs: Here’s What the Community Says

The data scientist community is divided over what is a better place for working – startups or MNCs. While some think that in SMEs, the designations don’t quite match the job they do, others believe small enterprises offer more to learn. MNCs, on the other hand, provide a clear roadmap for climbing up the ladder. The domain is still evolving, and professionals are trying to understand how the working conditions vary from one industry to the other.

Stanford pilots data science fellowship program

Standford is offering a data science fellowship program to eligible candidates in an attempt to take data science research ahead. Last summer, the university ran a pilot program to train students on data-driven solution discovery. This cohort of students had different educational background, but that did not stop them from learning the essentials of the domain and gather valuable findings. 

If you are interested in more such news on data science, check out our Data Science Roundup page.

AWS solution to build Real-time Data processing Application using Kinesis, Lambda, DynamoDB, S3

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A Capstone Project by Amit Bajaj and Sathya Guruprasad


Cloud Computing has become very popular due to the multiple benefits it provides and is being adopted by businesses worldwide. Flexibility to scale up or down as per the business needs, faster and efficient disaster recovery, subscription-based models which reduce the high cost of hardware, and flexible working for employees are some of the benefits of cloud that attracts businesses. Similar to cloud, Data Analytics is another crucial area which businesses are exploring for their growth. With the exponential rise in the amount of data available on the internet is a result of the boom in the usage of social media, mobile apps, IoT devices, sensors and so on. It has become imperative for the organisations to analyse this data to get insights into their businesses and take appropriate action.

AWS provides a reliable platform for solving complex problems where cost-effective infrastructure can be built with great ease at low cost. AWS provides a wide range of managed services, including computing, storage, networking, database, analytics, application services and many more. 

Problem Statement:

We have analysed multiple software solutions which perform analysis on data collected from the market and provide information as well as suggestions and provide better customer experience. This includes trade application providing stock price, taxi companies providing locations of nearby taxis, journey plan applications providing live updates on the different transport media and many more.

We have considered a “server-less” platform / “Server-less Computing Execution Model” to build the real-time data-processing app. Architecture is based on managed services provided by AWS.

What is “Server-less”?

A cloud-based execution model in which the cloud provider dynamically allocates and runs the server. This is a consumption-based model where pricing is directly proportional to consumer use. AWS takes complete ownership of operational responsibilities eliminating infrastructure management and availability with higher uptime. 

Services Consumed:

  1. Kinesis – Kinesis Data Stream- Kinesis Data Analytics- Kinesis Firehose
  2. Athena
  3. Lambda
  4. Dynamo DB
  5. Amazon S3
  6. AWS CLI


AWS solution to build Real-time Data processing Application - cloud computing

Without building a sizable infrastructure, how to receive data from different sources for cloud-based infrastructure?

Kinesis, a managed service by AWS, Amazon Kinesis makes it easy to collect, process, and analyse real-time, streaming data so you can get timely insights and react quickly to new information. Kinesis Datastream allows user to receive data from data generation source. We have created amazon kinesis data stream using AWS CLI commands which is expected to consume data from the data source.

Technical + Functional Flow 

Create Kinesis data streams: 

      1. Create a stream in Kinesis using AWS Console or AWS CLI Commands; one to receive data from Data generator and another to write post processing. Data generator will produce the data which will be read and written to input/source data stream. Kinesis Analytics App will process and write data to Output/destination stream.
      2. We have created a program to generate data, and with the help of AWS SDKs and AWS CLI commands transmitted to Kinesis Data Streams. Data can be generated in various fashion:
        1. Using IoT devices
        2. Live trackers
        3. GPS trackers
        4. API
        5. Data generator tools (in case of Analysis)

Create a Kinesis Analytics App to Aggregate data

      1. Build a Kinesis Data Analytics application to read from the input/source data stream and write to output/destination data stream in formatted fashion in a specified time interval.
      2. It is very important to stop the application when not in use to save unwanted cost.

Data Storage and Processing:

      1. Lambda, another managed service by AWS processes data from trigger data stream and write to dynamo DB
      2. Lambda function works on trigger basis and cost model is strictly driven by consumption. No cost is incurred from user when function is not running. Data is stored in Dynamo DB and can be accessed in standard fashion.

Kinesis Firehose, S3 and Athena:

    1. Kinesis Firehose acts as mediator between Kinesis Datastream and S3 where Data received from Kinesis Datastream will be predefined S3 bucket in specified format
    2. Amazon Athena is server-less interactive query service which enables user to glorify data stored in S3 Bucket for analysis. 

Amazon CLI, AWS Cloud formation and AWS IAM also plays a very important role in building Cloud based infrastructure and ensure secure connectivity within and outside AWS cloud world. 


Using AWS services, we were able to create a real-time data processing application based on serverless architecture which is capable of accepting data through Kinesis data streams, processing through Kinesis Data Analytics, triggering Lambda Function and storing in DynamoDB. The architecture can be reused for multiple data types from various data sources and formats with minor modifications. We have used all the managed services provided by AWS which led to zero infrastructure management efforts. 

Capstone project has helped us in building practical expertise on AWS services like Kinesis, Lambda, Dynamo DB, Athena, S3, Identity and Access Management, Serverless Architecture and Managed Services. We have also learnt the Go programming language to build pseudo data producer programs. AWS CLI has helped us to connect on-premise infrastructure with cloud services.  

This project is a part of Great Learning’s post graduate program in Cloud Computing. 

Amit Bajaj – Project Manager at Cognizant
Sathya Guruprasad – Infrastructure Specialist at IBM Pvt Ltd

Your Essential Weekly Guide to Data Science

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Data science enthusiasts are always looking for new developments and advances made in the field. With that in mind, we try to bring you data science news that showcase the most relevant trends for data science professionals. This week we have curated articles that talk about the learning resources, popular skills, job requisites, entrepreneurial ventures and more.  Read along.

10 Great Python Resources for Aspiring Data Scientists

Data scientists are often required to know more than one programming language. However, in most cases, Python is a language of choice for many. Since Python is extremely versatile, a thorough knowledge of this language helps data scientists in a number of tasks – hence its popularity. These Python resources are a great way of learning the language, especially if you are getting started in the field.

How to maintain your big data analytics software

When a company buys or subscribes to an analytics solution, keeping the work current can be a challenge. But maintaining that software is key to helping it enjoy a long, useful life. However, there are several challenges in the way to that. This article breaks down the essentials of analytics maintenance softwares so that companies can easily keep using those for a long period of time.

Why the newly minted data scientist wants a new job

Data science is one of the hottest jobs of the 21st century and professionals walking into this field often don’t require a strong background in technology. However, candidates picking up data science skills frequently face a mismatch of job expectations and reality. A large number of data scientists are looking for new jobs since they feel they are stuck with a job that does not utilise their skillset correctly.

Skills A Data Scientist Must Have To Land A Job: AIM Skills Study 2019

In a recently published report, AIM showcases the top skills required for a career in data science. Since data science is an evolving field, the trending subset of skills and tools keep changing every year. These skill sets mentioned in this article are the most relevant ones of 2019 and the upcoming years. Python has emerged as a majorly preferred programming language, while GPU hardware and CUDA knowledge have become essential to work with huge sets of data. 

Top 6 Priorities Data Science Startup Founders Shouldn’t Ignore

As an increasing number of data science experts opt for entrepreneurial ventures, startup companies flourish all over the country. Data science entrepreneurship requires a lot of patience, perseverance and above all, knowledge of the latest trends of the domain. This article lists a number of data science priorities that professionals should not ignore in order to achieve success. 

If you are looking for similar articles on data science, keep an eye on this space. We bring you more!

Your Essential Weekly Guide to Data Science and Analytics- September Part III

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Owing to its diverse set of applications, data science has emerged as one of the most in-demand career paths for young professionals. Upskilling in data science will certainly set you in the path of a high-flying career. However, upskilling alone is never enough in an ever-evolving domain like data science. You have to closely follow the latest trends and technological developments – which we understand can be quite a task sometimes, which data science blogs to follow, which trends to watch? We have put together a list of news articles that highlight the most impactful developments and trends. Read through to stay updated.

Top 10 Data and Analytics Trends to Watch Out in 2020

With 2020 already a mere last quarter away, data science enthusiasts are trying to look into the trends that will dominate the domain in the coming year. Augmented Analysis is among the top trends of data science in 2020. It combines ML and AI applications to change how analytics content is created, devoured and shared. The augmented analysis will be driving data science, ML platforms and embedded analytics. Data analysis automation, continuous intelligence, NLP and conversational analytics are among the other data science trends that will take over the market.

Explorium secures $19M funding to automate data science and machine learning-driven insights

Explorium, an Israel based startup has received a $19 million funding to work towards automating its data science and machine learning platforms. “Just as a search engine scours the web and pulls in the most relevant answers for your need, Explorium scours data sources inside and outside your organization to generate the features that drive accurate models”, says co-founder and CEO Maor Shlomo. The company works in three stages- data enrichment, feature engineering and predictive modelling to help companies derive insights and add features to their applications

Data science safeguards digital transactions

The mass adoption of smartphones and other smart devices has lead to the digitization of money worldwide. Philippines is among the countries to head towards a digital economy steadfastly. While digitization of money brings convenience, it also exposes organizations and individuals to cyber crimes. Digital payment platforms are focusing on ways to secure financial transactions from any kind of cyber attack. Data science applications are helping in analysing and predicting ways of doing that.

“For consumers, there is really no substitute to awareness,” said Nagesh Devata, general manager of Southeast Asia Cross Border Markets for PayPal. “For enterprises, we need to move from reactive to predictive models in risk management.”

Which Data Science Skills are core and which are hot/emerging ones?

Recent surveys revealed that professionals think that 30 data science skills are the most coveted in data science resumes. Tensor flow, deep learning, Apache Spark, Pytorch are among the top data science tools and skills that are becoming increasingly relevant today. SQL and ETL Data Preparation, on the other hand, are losing popularity as more advanced technologies and tools are taking over.

University of Virginia’s data science school gets state approval

University of Virginia has finally received an approval for its data science school from the State Council of Higher Education for Virginia. “I am delighted that the School of Data Science has cleared its final hurdle and can officially move forward,” said UVA President Jim Ryan in a statement. “I want to thank the State Council of Higher Education for Virginia for sharing our excitement in this proposal, and Phil Bourne and his team at the Data Science Institute for their hard work.” The approval was pending for 8 months after they had received a $120 million donation.

If you found these articles to be interesting then head over to our Data Science blog to get more updates.

The placement team was an important part of the program – Diptanil Bhowmik, Consultant Analyst at Fractal Analytics

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Given the present job market scenario, it becomes difficult for freshers to get a job even if they know what they want to pursue as a career. In such a situation it is important to figure out the right path to land your dream job and get the relevant skills required for the job. Upskilling with Great Learning was the path that Diptanil Bhowmik chose and was successful in achieving a job as consultant analyst at Fractal Analytics. Read more to know what he has to say about the Data Science program he pursued.

What did you like the most about Great Learning?

To start with, Great Learning has been a great experience due to the faculty appointed for the program. They are highly skilled and have a great depth of knowledge on the topics being taught. Secondly, the management was quite considerate.

The initial classes were easy but as the major topics came in, it needed an extra effort and the whole management was very helpful with extra classes and other guidance as required. 

The best part of the program is the capstone project that I have worked on. I got an IPL dataset to work on which was both interesting and challenging. Through this project, I got to learn how data science works.

The placement team was an important part of the program as many top-notch companies were brought in and a placement boot camp was set-up for each company.

I got an offer from Fractal Analytics as a consultant analyst which gave a great career boost to me being a fresher. The company is great to work for. 

How was your experience of the interview process at Fractal?

Fractal had one of the most rigorous placement processes. It started with an aptitude test which required us to answer 70 questions in 75 minutes. A safe score would be around 40-45.

After that, there was an SQL+Python test in Hacker Rank which was quite challenging.

I got called for the personal interview round after clearing the previous rounds. From this point, every round was an elimination round which consisted of a Technical round which was a resume drilling round, followed by a business problem round that involved case studies and guesstimate questions, and lastly an HR round.

How did GL help you with the interview process?

GL helped with all these rounds. Different companies have different requirements and GL made sure to prepare us for each interview process separately. 

The experience with GL had been excellent and I would recommend people for taking up the DSE course because of the quality of content they have and the depth they teach with. They also provide tasty food for lunch if you are a foodie.

Upskill with Great Learning’s PG Program in Data Science and Engineering and unlock your dream career.

The placement team helped us to stay motivated throughout the process – Aashish Anil Mishra, Consultant at Fractal Analytics

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We see many professionals getting disheartened by multiple rejections during the process of job search. But the few who do not get demotivated and look out for feedback and ways to improve themselves to finally crack their dream jobs, are the ones worth sharing their experience with others. Read how Aashish Anil Mishra learnt from his feedback, upskilled, and cracked a job with Fractal Analytics.  

When did you decide to upskill?

Right after completing my post-graduation as an MBA, I started looking for a job. I got interviewed for a few roles and also got selected in some of them. But I did not find job roles matching my career preferences.

Soon, I got a call for an Analyst role from Ugam. This was the role which I would have preferred, but I could not get through the interview process. I started applying for similar open positions with other analytics companies via different mediums but did not get an interview call. I then received a mail stating that the positions are filled by the candidates with comparatively better skills and experience.

So, I got to know that I need to develop my skill set with the most common requirement of the analytics market. 

Why did you choose Great Learning?

It occurred to me that I had to learn the tools which I had never heard about earlier. I started exploring the internet and went through the online courses from a few platforms like Udemy and Analytics Vidhya but was not fully convinced with the format.

I needed a classroom training program, which would help me in developing skills and career transition. I searched for options and found PGP-DSE offered by Great Learning as the best one based on reviews and career transition ratio. After completing the pre-joining requisites, i.e., the online exam and interview, I enrolled in PGP-DSE Jan’19 batch at Bangalore.

What was the role of gurus and teams in making sure you have a great experience?

Initially, I found it difficult because I had very less prior exposure to programming. But as the sessions progressed, it became easier because of classroom training, rigorous practice, take-home lab exercises, and support from the colleagues. As the batch progressed, new tools were introduced by the faculty in a manner which helped even first-timers

The gurus arranged for the different sessions to enable domain expertise, so it was quite easy to get the silliest of the doubts cleared. The structure of the program was super which helped in properly learning the things. Periodic exams helped to stay in touch with the topics already covered. There were teaching assistants who were there to help any time, which was the best part as doubt clearing process did not take time.

The program support team, the soft skill development team, and the placement team were all helpful throughout the program. Bootcamp arranged before the placements helped in many ways as it covered everything in a short period, which was kind of a revision.

How did the placement team help with scoring a job?

We were all ready for the placements [the purpose for which almost everyone joined the program]. Many companies started conducting drives, some got through the process and some had to wait for other interviews. The placement team helped us to stay motivated throughout the process. I got an opportunity to attend an interview in 3 companies before getting placed in Fractal Analytics as a Consultant.

Overall the journey was good and I would like to thank Great Learning for developing the right set of skills and helping me in the career transition. Also, I would like to thank the program support team who were always there to take up the queries and help in the best possible way for the same. I would also like to mention that you get connected to the huge alumni network of Great Learning which will be a great help in future too.

Upskill with Great Learning’s PG Program in Data Science and Engineering and unlock your dream career.

What is Data Science?

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Data Science continues to be a hot topic among skilled professionals and organizations that are focusing on collecting data and drawing meaningful insights out of it to aid business growth. A lot of data is an asset to any organization, but only if it is processed efficiently. The need for storage grew multifold when we entered the age of big data. Until 2010, the major focus was towards building a state of the art infrastructure to store this valuable data, that would then be accessed and processed to draw business insights. With frameworks like Hadoop that have taken care of the storage part, the focus has now shifted towards processing this data. Let us see what is data science, and how it fits into the current state of big data and businesses. 

Broadly, Data Science can be defined as the study of data, where it comes from, what it represents, and the ways by which it can be transformed into valuable inputs and resources to create business and IT strategies. 

what is data science graphical representation(Source: datascience@berkeley)


Why businesses need Data Science?

We have come a long way from working with small sets of structured data to large mines of unstructured and semi-structured data coming in from various sources. The traditional BI tools fall short when it comes to processing this massive pool of unstructured data. Hence, Data Science comes with more advanced tools to work on large volumes of data coming from different types of sources such as financial logs, multimedia files, marketing forms, sensors and instruments, and text files. 

Mentioned below are relevant use-cases which are also the reasons behind Data Science becoming popular among organizations:

– Data Science has myriad applications in predictive analytics. In the specific case of weather forecasting, data is collected from satellites, radars, ships, and aircraft to build models that can forecast weather and also predict impending natural calamities with great precision. This helps in taking appropriate measures at the right time and avoid maximum possible damage. 

– Product recommendations have never been this precise with the traditional models drawing insights out of browsing history, purchase history, and basic demographic factors. With data science, vast volumes and variety of data can train models better and more effectively to show more precise recommendations.

– Data Science also aids in effective decision making. Self-driving or intelligent cars are a classic example. An intelligent vehicle collects data real-time from its surrounding through different sensors like radars, cameras, and lasers to create a visual (map) of their surroundings. Based on this data and advanced machine learning algorithm, it takes crucial driving decisions like turning, stopping, speeding etc. 


What is data science


Why you should build a career in Data Science? 

Now that we have seen why businesses need data science in the above section, let’s see why is data science a lucrative career option through this video:


Who is a Data Scientist?

A data scientist identifies important questions, collects relevant data from various sources, stores and organizes data, decipher useful information, and finally translates it into business solutions and communicate the findings to affect the business positively. 

Apart from building complex quantitative algorithms and synthesizing a large volume of information, the data scientists are also experienced in communication and leadership skills, which are necessary to drive measurable and tangible results to various business stakeholders. 


What is the prerequisite skill sets to Data Science?

Data Science is a field of study which is a confluence of mathematical expertise, strong business acumen, and technology skills. These build the foundation of Data Science and require an in-depth understanding of concepts under each domain. The three requisite skills are elaborated below:

Mathematical Expertise: There is a misconception that Data Analysis is all about statistics. There is no doubt that both classical statistics and Bayesian statistics are very crucial to Data Science, but other concepts are also crucial such as quantitative techniques and specifically linear algebra, which is the support system for many inferential techniques and machine learning algorithms. 

Strong Business Acumen: Data Scientists are the source of deriving useful information that is critical to the business, and are also responsible for sharing this knowledge with the concerned teams and individuals to be applied in business solutions. They are critically positioned to contribute to the business strategy as they have the exposure to data like no one else. Hence, data scientists should have a strong business acumen to be able to fulfil their responsibilities. 

Technology Skills: Data Scientists are required to work with complex algorithms and sophisticated tools. They are also expected to code and prototype quick solutions using one or a set of languages from SQL, Python, R, and SAS, and sometimes Java, Scala, Julia and others. Data Scientists should also be able to navigate their way through technical challenges that might arise and avoid any bottlenecks or roadblocks that might occur due to lack of technical soundness.


Other roles in the field of data science:

So far, we have understood what is data science, why businesses need data science, who is a data scientist, and what are the critical skill sets that are required to enter the field of data science. Now, let us look at some other data science job roles apart from that of a data scientist:

– Data Analyst: This role serves as a bridge between business analysts and data scientists. They work on specific questions and find results by organizing and analyzing the given data. They translate technical analysis to action items and communicate these results to concerned stakeholders. Along with programming and mathematical skills, they also require data wrangling and data visualization skills. 

– Data Engineer: The role of a data engineer is to manage large amounts of rapidly changing data. They manage data pipelines and infrastructure to transform and transfer data to respective data scientists to work on. They majorly work with Java, Scala, MongoDB, Cassandra DB, and Apache Hadoop. 


Data Science Salary trends across job roles:

what is data science

(Source: Analytics India Magazine – Salary Study 2019)


Who can become a data scientist/analyst/engineer?

Data Science is a multidisciplinary subject and it is a big misconception that one needs to have a PhD in science or mathematics to become a data science professional. Although a good academic background is a plus when it comes to data science profession, it is certainly not an eligibility criterion. Anyone with a basic educational background and an intellectual curiosity towards the subject matter can become a data scientist. 


Critical tools in Data Science Domain:

SAS – It is specifically designed for operations and is a closed source proprietary software used majorly by large organizations to analyze data. It uses the base SAS programming language which is generally used for performing statistical modelling. It also offers various statistical libraries and tools that are used by data scientists for data modelling and organising. 

Apache Spark – This tool is an improved alternative of Hadoop and functions 100 times faster than MapReduce. Spark is designed specifically to manage batch processing and stream processing. Several Machine Learning APIs in Spark help data scientists to make accurate and powerful predictions with given data. It is a highly superior tool than other big-data platforms as it can process real-time data, unlike other analytical tools which are only able to process batches of historical data.

BigML – BigML provides a standardized software using cloud computing, and a fully interactable GUI environment that could be used for processing ML algorithms across various departments of the organization. It is easy to use and allows interactive data visualizations. It also facilitates the export of visual charts to mobile or IoT devices. BigML also comes with various automation methods that aid the tuning of hyperparameter models and help in automating the workflow of reusable scripts. 

D3.js – D3.js is a javascript library that makes it possible for the user to create interactive visualizations and data analysis on their web browser with the help of its several APIs. It can make documents dynamic by allowing updates on the client-side, it actively uses the change in data to reflect visualization on the browser. 

MATLAB – It is a numerical computing environment that can process complex mathematical operations. It has a powerful graphics library to create great visualizations that help aid image and signal processing applications. It is a popular tool among data scientists as it can help with multiple problems ranging from data cleaning and analysis to much advanced deep learning problems. It can be easily integrated with enterprise applications and other embedded systems. 

Tableau – It is a Data Visualization software that helps in creating interactive visualizations with its powerful graphics. It is suited best for the industries working on business intelligence projects. Tableau can easily interface with spreadsheets, databases, and OLAP (Online Analytical Processing) cubes. It sees a great application in visualizing geographical data. 

Matplotlib – Matplotlib is developed for Python and is a plotting and visualization library used for generating graphs with the analyzed data. It is a powerful tool to plot complex graphs by putting together some simple lines of code. The most widely used module of the many matplotlib modules is the Pyplot. It is an open-source module that has a MATLAB-like interface and is a good alternative to MATLAB’s graphics modules. NASA’s data visualizations of Phoenix Spacecraft’s landing were illustrated using Matplotlib.

NLTK – It is a collection of libraries in Python called Natural Language Processing Toolkit. It helps in building the statistical models that along with several algorithms can help machines understand human language. 

Scikit-learn – It is a tool that makes complex ML algorithm simpler to use. A variety of Machine Learning features such as data pre-processing, regression, classification, clustering, etc. are supported by Scikit-learn making it easy to use complex ML algorithms. 

TensorFlow – TensorFlow is again used for Machine Learning, but more advanced algorithms such as deep learning. Due to the high processing ability of TensorFlow, it finds a variety of applications in image classification, speech recognition, drug discovery, etc. 

If you are interested in pursuing a career in Data Science, check out Great Learning’s postgraduate program in Data Science and Business Analytics. The Analytics and Data Science course from Great Learning has been ranked No.1 consistently since 2014 by Analytics India Magazine. The program provides international recognition and dual certificate from the University of Texas at Austin, McCombs School of Business and Great Lakes, India. You will get to learn from the top-ranked data science faculty along with the flexibility to learn at your time and space with the online learning and weekend classes format.

Your weekly Guide to Data Science and Analytics – September Part I- GL

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Data Science and Analytics are being applied across industries, varying in their scope and magnitude based on the purpose of the application. Even as we witness these technologies solving bigger problems, there are still some challenges faced while building these solutions. We explore some of those challenges in this week’s digest.  

SEBI Bets on Data Analytics, New Generation Tech to Address Market Challenges

Continuing its efforts to bolster supervision and identify non-compliance, regulator Sebi plans to deploy data analytics and new generation technologies to deal with various challenges in the market. Technology solutions are being built to achieve the objective of identifying non-compliance and assisting in investigations.

Figleaves Deploys AVORA Augmented Analytics for Granular Insights and Reporting

AVORA provides an end-to-end augmented analytics platform, utilising Machine Learning with smart altering to deliver easy to use, in-depth data analysis. By eliminating the limitations of existing analytics, reducing data preparation and discovery time by 50-80%, and accelerating time to insight to just a matter of seconds rather than days, AVORA creates game-changing organizational intelligence.

New Tools of Data Science Used to Capture Single Molecules in Action

Single-molecule fluorescence techniques have revolutionized our understanding of the dynamics of many critical molecular processes, but signals are inherently noisy and experiments require long acquisition times. This work leverages new tools from data science in order to make every photon detected count and refine our picture of molecular motion.

Challenges in Analytics Sector: The Industry Perspective

Analytics industry has witnessed significant growth over the years but is still prone to a lot of challenges in terms of talent, reaching the right consumers, cumulating data points, among others. 3 Key Challenges That Analytics Industry Still Faces Today are: 

Translating data to business impact | Multiple sources of data | Data quality

To read more about Data Science, Analytics, and their career prospects, check this space. Upskill in Data Science domain with Great Learning’s PG program in Data Science and Engineering.

Difference Between Data Science & Business Analytics

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Data Science vs Business Analytics, often used interchangeably, are very different domains. A layman would probably be least bothered with this interchangeability, but professionals need to use these terms correctly as the impact on the business is large and direct. In this article, we will elaborate on the difference between the two.  

Learn about the Course


Data Science and Business Analytics are unique fields, with the biggest difference being the scope of the problems addressed. Simply put, The science of data that uses algorithms, statistics, and technology is known as Data Science. It provides actionable insights on a range of structured and unstructured data solving a broader perspective such as customer behaviour. 

Difference between Data Science and Business Analytics

On the other hand, the statistical study of mostly structured business data is known as Business Analytics. It provides solutions to specific business problems and roadblocks. 

These two terms are interchangeably used in either of the above scenarios, i.e., a business analytics problem could be wrongly addressed to be solved with the help of Data Science. The implications of carelessly using the term ‘Data Science’ in this context could be adverse because the tools and techniques used in Business Analytics are different than Data Science and using wrong tools to assess a data set will yield imperfect and undesirable results. 

Data Science is an umbrella term for all things dedicated to mining large data sets. An intersection of programming, statistics, and data analytics, Data Science is not limited to only statistical or algorithmic aspects. Business Analytics is the end-product of data science. It includes two broad categories, that are Statistical Analysis and Business Intelligence. 

Difference between Data Science and Business Analytics

Business Intelligence

Another term often confused with Data Science is Business Intelligence. It is also an umbrella term that portrays ideas and strategies to improve decision making by utilizing fact-based support systems. Modern Business Intelligence is much beyond just business reporting. It is a mature framework that encompasses intuitive dashboards, mobile analytics, what-if planning, etc. It additionally incorporates enormous back-end machinery for maintaining control around reporting.

Although it sounds similar to Data Science, it is not. The principal difference lies in the type of problems that they address. Business Intelligence deduces the new unknown values of previously known elements using a formula that is already available. On the other hand, Data Science works with unknown scenarios without any formula or algorithm in hand, to solve data queries that nobody has ever answered in the past. Data Science problems are solved by exploring data, finding the best method, building a model around it, and finally operationalizing the model. 


Business Intelligence is well established with deep roots in a typical corporate landscape. Corporate professionals are familiar, comfortable, and confident with the BI concepts and framework. As BI projects work on known unknowns, the projects can be planned well in advance and timelines could be efficiently followed. Also, there is minimal trial and error with several successful BI projects in a company’s kitty, who would have developed good project expertise over the years. 

There is a massive career scope in the fields of Business Intelligence and Business Analytics. Professionals who are genuinely thinking of making a shift in the BA and Data Science roles can consider upskilling with the right course. Great Learning’s PG program in Data Science & Business Analytics and helps working professionals make a smooth and successful transition. The course offers the choice of online or classroom-based learning with Dual Certificate from University of Texas at Austin, McCombs School of Business (world rank #2 in Analytics), and Great Lakes (India rank #1 in Analytics). It helps you with hands-on practical learning with case studies and projects, without the need of quitting your job. The course is also tailor-made keeping in mind the professionals from the non-IT background. With our career guidance and support, you can easily land your dream job in Business Intelligence and Business Analytics.