Data Science

Top Data Science Courses 2023

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Businesses run on data. Every day they are looking to leverage vast chunks of data to make informed business decisions and device smart solutions. To do this, they need Data Scientists.

Before you get confused, Data Scientists and Artificial Intelligence Engineers are not the same. They may have overlapping job roles but differ in more than one way.

Also Read: Data Scientist vs. AI Engineer

For instance, if a company wants to improve its sales revenue, it will hire a data scientist to analyze business performance data and make the right decision to maximize it.

The pressing need for a data scientist in almost every business and industry has prompted this field to become one of the most sought-after job titles. If you are still wondering if you should consider learning Data Science, then here’s what you need to know – companies are pushing boundaries to hire skilled Data Scientists, even if it means shelling out astronomical figures to close these positions.

As you know, the skill gap in this field has resulted in more vacant positions than skilled Data Scientists. 

Understanding the Data Science Industry Growth

As the world moves into the realm of digitalization, more and more data is being generated. This, in turn, is opening doors to increased demand for Data Science technology and tools. To improve productivity, and make sense of this vast data set, big data, data mining, machine learning, data science, and data analytics are forming the backbone of leading businesses worldwide.

Be it any industry, domain, or field, the surge in demand for skilled Data Scientists is proof enough that this industry will have unprecedented growth. In 2018, we said that the world would need one million Data Scientists.

 Fast-forward to three years now – According to the latest market research report, the valuation of the Data Science industry is predicted to touch $140.9 billion by 2027. 

In 2019, it was recorded to touch $37.9 billion. These statistics for Data Science proof of how the market is anticipated to grow.

Nothing can slow down this growth, not even a Pandemic like Covid-19 that has hit the world in its worst forms.

It is important you know when it comes to where the Data Science industry is heading. This will help you determine what to learn, where, and how to learn. It takes no Einstein to predict that the growth of the Data Science industry isn’t faltering any time soon. Interpretability continues to grow in leaps and bounds.

How can I say this?

In 2019, Salesforce acquired Tableau, while Google expanded its horizon with Looker. These two were the biggest deals that prompted experts to believe in the steady growth that Data Science will have. Apart from these two, many more small acquisitions happened –

These are just a few acquisitions I mentioned. 2019 saw an intensive M&A activity and consolidation in the data science industry.

The idea of leading names trying to leverage their assets by bringing in further consolidation in the data science market points out that these names want to expand their capabilities to leverage AI better. 

This is your ideal time to get started with Data Science Certification courses.

Data Science Courses: Eligibility

You already know what data science is by definition since you are here looking up related courses. So I will skip the definition part.

To get started with Data Science courses, you must first understand if you have the eligibility. It is not always about your specialization in your higher studies or graduation. Eligibility to learn Data Science also requires reasoning skills. Technicalities can be learned with perseverance, but logical reasoning and eye-for-details are something that you need to have to get started.

Data Science has four core elements –

  1. To acquire information
  2. To organize the data
  3. To discover trends
  4. To help device future course of actions

Data Scientists take upon the responsibility of taming unstructured data and creating meaning. The four elements drive a Data Scientist to create business-driven decisions.

Analyzing your eligibility is essential. As I said, your past qualification will not determine if you want to take a data Science Certification Course. For instance, Siva Devarakonda, the director of Data Science for Noodle.ai, has a background in electrical engineering and later took up a data science online course. Siva believes that Data Science cannot be taught from scratch. When you are going through a Data Science Syllabus, you realize the areas you want to learn or master.

To check where you stand, start with the following questions:

  1. What skills you will need?
  2. What is your current skill status?
  3. What kind of job role do you want to explore?

You’ll know you are ready to learn in real time when you have specific answers to these three questions.

If you are still looking blank, I will give you a context to start analyzing your skills and eligibility. To become a Data Scientist, you can opt for a university-based course (if you have the time and money), or you can opt for some of the professional Data Science certification courses. There are many free Data Science courses as well (which we will discuss in detail later).

Whatever mode of learning you pick, your starting point is here. Check out if you have these skills to get started:

  • Knowledge in Statistics

Statistics for Data Science is more like oxygen for living. You cannot afford to excel in Data Science if Statistics is not your friend. If someone asks me, ‘Hey, how to become a Data Scientist?’ I’d say, ‘Get a grip on your Statistics’.

Why?

Statistics form the backbone of data science. It is what helps in structuring vast chunks of data and deriving useful pieces of information from it. With Statistics, you can process all the information and predict business outcomes successfully.

You may also like to read: Basic Statistics Concepts for Data Science

  • Mathematics for Data Science (and Machine Learning)

Practical implementation comes in the form of a skilled mathematician. If you are scared of mathematics, you either opt out of Data Science or eliminate your fear.

You have to understand that Data Science and Machine Learning are inter-dependable concepts. Despite being a variant in treatments, they both work together. You will need mathematics to optimize machine learning principles as a data scientist.

  • Programming Languages

There are a few programming languages that are necessary to get started with a data science course.

  1. Microsoft Excel – Advanced Knowledge.
  2. Python
  3. R
  4. SQL

Data Science is a vast field. To master it, you must know all these underlying fields.

For more details, check out this interactive dashboard that combines education qualifications and job roles of over 1001 Data Scientists across 3 years [2018-2020].

Research into Data Science Skills

Essential Resources to Get Started With Data Science

Let’s get you into your learning robes without much ado. The first step is getting your hands on the right resources. We will break this into two sections:

  1. Data Science Skills
  2. Data Engineering Tools

A complete Data Science Certification course is step two. I have put together some amazing reading resources to get started with Data Science.

1. Data Science Skills – Resources to get started with

For easier understanding, I will break this section into five categories: Foundation Skills, Technical Skills, Business Skills, Miscellaneous Skills, and Practice resources.

(1) Foundation Skills

The first answer to How to Become a Data Scientist lies in learning the two foundation skills:

Programming and Data Wrangling

To acquire this skill, you will have to know at least one kind of scripting language. Ideally, I recommend you start with Python or R. Whichever you choose, ensure you do one thing at a time.

To decide which is better, read our guide on Python vs. R.

Python Resources to get started with:

R Resources to get started with:

  • Probability and Statistics

I have already discussed the importance of Statistics in learning Data Science. This skill helps you with A/B testing, conditional probability, and many other skills. Resources to start with:

(2) Technical Skills

You will require several technical skills to convert raw data into something meaningful. Let’s take a look at these skills:

Data Collection:

You will always need the right data set to make the right inferences. Data Collection can be done in four ways –

  • Handling internal data that belongs to your company is collected through various business operations. Such data is usually relevant.
  • Online data sets that let you prototype before you invest in copyrighting it.
  • API to legally and programmatically access data sets that are collected by other companies.
  • Using Web crawling and Web scraping tools needs to be done responsibly.

Resources to get started with Data Collection:

SQL:

We have already talked about SQL and resources to get started with.

Data Visualization:

This skill is helpful for the explanatory analysis of data. You can start with Data Visualization with R or Python.

Applied Machine Learning:

Machine Learning Scientist and Data Scientist are two different job roles, but they are still related. Machine learning is a large concept that encompasses sub-concepts like Data Science. The resource you can use is Machine Learning by Andrew Ng – A video series to learning machine learning.

(3) Business Skills

Business skills are also soft skills that you must have to become a skilled data scientist. Being a data scientist is not only about learning technical know-how. You must also have the aptitude to lead. You will need to understand how a business works, how it envisions its value proposition, and how you can creatively think of solutions and effectively communicate your ideas. Below are some of the soft skills that you must have. Follow the resources mentioned for each skill.

  • Communications:

There is no substitute for this skill. If you cannot communicate your ideas, chances are someone else will. Often data scientists face challenges when it comes to effectively communicating mathematical and technical solutions. In this scenario, you will need to be precise, clarify your explanation, and align your ideas with the organization. I will recommend a TED talk titled The Best Stats you’ve ever seen. This TED talk will show you how you can communicate data in a fun way. Not everything needs to be boring!

  • Innovative bend of mind:

Data Scientists are supposed to tame unstructured data and make valuable use of data. This requires creative thinking and an innovative approach. After all, there is more than one way of solving a problem. It is up to you how best you can find the most effective solution. Follow this TED talk on The Art of Innovation by Guy Kawasaki for a better learning curve.

  • Business Strategy Building:

Another core job role of a data scientist. You are not responsible for only analyzing data; you are also supposed to help make improve business decisions. Hence, it is inevitable that you are expected to have better business strategy knowledge than anyone else. Watch the TED talk How Data Will Transform Business to understand the correlation between technology and business.

(4) Miscellaneous Skills

These are more in-demand skills. You can learn these at your convenience.

  • Natural Language Processing (NLP): Follow the video series from Stanford NLP.
  • Recommendation Systems: Also known as Collaborative Filters, this skill has the most success in the eCommerce domain. Follow the Recommendation engine Tutorial for an introduction to advanced learning.
  • Time Series Analysis: This involves indexing data by time. Read The Little Book of R for Time Series – an online book for step-by-step learning.

(5) Practice Resources

Practice resources serve two major purposes:

It helps you practice all the concepts you have learned and turn you into a better data scientist.

You have projects to show your employers, giving you an edge over others.

Check out our list of 20 project ideas that you can try.

2. Resources for top Data Engineering Tools

1. Apache Spark

Apache Spark, or Spark, is the god of analytics engines. When you work as a Data Scientist Professional, you will need this tool in some job profiles. Here are some good reading resources to get yourself acquainted.

2. MySQL

SQL (pronounced as Sequel) is a computer programming language used by database software. MySQL and PostgreSQL are separate types of databases that use SQL language. Start learning MySQL with these free resources:

Realted: Master SQL For Data Science – Getting Started!

Let’s move on to proper data science courses.

Top Data Science Courses to Apply for in 2023

You now know the prerequisites, business, and soft skills to become a data scientist. Let’s look at full-fledged data science courses offered by various online platforms and recognized universities.

You can start with Data Science Certification courses if you want to learn at your own pace. These are well-structured and give you a valid certificate upon completion.  Here are the top three best online data science courses in India.

1. Data Science with Python and ML – AnalytixLabs

Overview:

Course Duration: 220 Hours (Around 4 months)

If you are looking for a Data Science course in India that will help you specialize in various elements of Data Science & Machine Learning, then this is the right course for you. The course begins with the foundation concepts of Python for data manipulation & analysis and statistical analysis and graduates up to advance techniques using machine learning.

Teaching mode: Classroom + Online interactive learning

Classroom Training is available in Delhi NCR (Gurgaon & Noida) and Bangalore, India. If you are anywhere other than these two cities of India, you can avail of the interactive online training material to learn at your own pace.

Curriculum:

  • Python Foundation

    • Introduction to Data Science with Python
    • Python: Essentials (Core)
    • Scientific Distributions
    • Accessing, Importing and Exporting Data
    • Data Manipulation -Cleansing and Munging
    • Data Analysis – Visualization Using Python

  • Machine Learning:

    • Introduction to Statistics
    • Introduction to Predictive Modelling
    • Data Exploration for Modeling
    • Data Preparation for Modeling
    • Segmentation: Solving segmentation Problems
    • Linear Regression: Solving Regression Problems
    • Logistic regression: Solving Classification Problems
    • Time Series Forecasting: Solving Forecasting problems
    • Machine Learning – Predictive Modeling (basics)
    • Unsupervised Learning: Segmentation
    • Supervised Learning: Decision Trees
    • Supervised Learning: Artificial Neural Networks (ANN)
    • Supervised Learning: Support Vector Machines
    • Supervised Learning: Naïve Bayes
    • Project: Consolidate Learning
  • Text Mining NLP/NLG

    • Text Analytics
    • Natural Language Processing

[Access to Course Module: Available up to 1 year after course completion.]

Course Fees:

  • Instructor-Led Live Classes: INR 32,000 + taxes
  • Video-Based Self-learning: INR 27,000 + taxes
  • Demo Class: Free Access

2. Advanced Big Data Science – AnalytixLabs

Overview:

Course Duration: 380 Hours (Around 6 months)

This extension of the Data Science & Machine Learning course includes sought-after Big Data & Cloud computing skills. These skills are covered along with in-demand tools, like Python, Hadoop-Spark, SQL, No-SQL, etc.

Teaching mode: Classroom + Online interactive learning

Classroom Training is available in Delhi NCR (Gurgaon & Noida) and Bangalore, India. If you are anywhere other than these two cities of India, you can avail the interactive online training material to learn at your own pace.

Curriculum:

  • Python Foundation

    • Introduction to Data Science with Python
    • Python: Essentials (Core)
    • Scientific Distributions
    • Accessing, Importing, and Exporting Data
    • Data Manipulation -Cleansing and Munging
    • Data Analysis – Visualization Using Python

  • Machine Learning:

    • Introduction to Statistics
    • Introduction to Predictive Modelling
    • Data Exploration for Modeling
    • Data Preparation for Modeling
    • Segmentation: Solving segmentation Problems
    • Linear Regression: Solving Regression Problems
    • Logistic regression: Solving Classification Problems
    • Time Series Forecasting: Solving Forecasting problems
    • Machine Learning – Predictive Modeling (basics)
    • Unsupervised Learning: Segmentation
    • Supervised Learning: Decision Trees
    • Supervised Learning: Artificial Neural Networks (ANN)
    • Supervised Learning: Support Vector Machines
    • Supervised Learning: Naïve Bayes
    • Project: Consolidate Learning

  • Text Mining NLP/NLG

  • Text Mining Analytics

[Access to Course Module: Available up to 1 year after course completion.]

Course Fees:

  • Instructor-Led Live Classes: INR 48,000 + taxes
  • Video-Based Self-learning: INR 43,000 + taxes
  • Demo Class: Free Access

3. Advanced Management Programme in Business Analytics (AMPBA)

Course Provider: ISB

Overview:

This program is designed for both new learners and working professionals. However, you must commit to learning this course within a specific schedule.

Teaching Mode: Classroom + online learning

Classroom Training every alternate month for 12 months. In the months when no classroom sessions happen, participants will access course modules online. Participants taking this course must be on campus for the months when classroom sessions are held.

Curriculum:

There are a Foundation Term and six more terms in this curriculum.

  • Foundation Term:

    • Probability and Statistics using R
    • Introduction to Python

  • Term 1:

    • Data Collection
    • Data Visualization
    • Statistical Analysis-1
    • Foundation of Big Data Management
    • Business Communications and Storytelling

  • Term 2:

    • Optimization
    • Text Analytics
    • Statistical Analysis-2
    • Advanced Topics in Big Data (MapReduce, Hadoop, Spark, Pig, Hive)
    • Foundational Project-1

  • Term 3:

    • Machine Learning (Unsupervised Learning-1)
    • Big Data Applications
    • Advanced Optimization and Simulation
    • Multivariate Analysis
    • Contemporary Topics-1

  • Term 4:
    • Machine Learning (Unsupervised Learning-2)
    • Machine Learning (Supervised Learning-1)
    • Forecasting Analytics
    • Count Data and Survival Analysis
    • Foundational Project-2

  • Term 5:

    • Machine Learning (Supervised Learning-2)
    • Deep learning
    • Customer Analytics
    • Financial Analytics
    • Contemporary Topics-2

  • Term 6:

    • Applications of Artificial Intelligence
    • Digital Media Analytics
    • Supply Chain Analytics
    • Pricing Analytics
    • Contemporary Topics-3

  • Capstone Project

Fees:

  • Enrolment Fee: INR 1,000 – non-refundable.
  • Program Fee: INR 9,65,000 + taxes. This includes admission fees, tuition fees, course materials, and campus accommodations.

  • Other Fees:
    • AMP Alumni FeeL INR 15,000 + taxes
    • Refundable Security Deposit: INR 10,000

The application requires candidates to have:

  • Bachelor’s or Master’s degree in Engineering, Technology, Statistics, Mathematics, Science, or Equivalent.
  • Minimum of 2 years of full-time work experience

Note: Freshers can apply too.

Enrolment: Requires clearance of entrance test.

An exemption is available if the candidate can submit GMAT/GRE/CAT/NMAT score before the test for approval.

Entrance test: July 12, 2020, for Winter 2021 batch.

4. Business Analytics and Intelligence

Course Provider: Indian Institute of Management, Bangalore.

Overview:

This course is full-fledged on-campus learning. It is designed to teach details of business analytics tactics and their application in business.

Teaching Mode: Classroom Training

Off-campus learning mode is available for limited candidates. Candidates outside Bangalore can opt for Online classes, but there are many restrictions and to-dos for such classes. Please follow this link to learn the details.

Curriculum:

There are 10 modules in this course.

  • Module-1
    • Foundations of Data Science:
    • Data Visualization and Interpretation (6 days)

  • Module-2
    • Data Preprocessing and Imputation (2 days)

  • Module-3
    • Predictive Analytics: Supervised Learning Algorithms (6 Days)

  • Module-4
    • Optimization Analytics (Prescriptive Analytics (5 Days)

  • Module-5
    • Stochastic Models

  • Module-6
    • Data Reduction, Advanced Forecasting and Operations Analytics (5 Days)

  • Module-7
    • Big Data Analytics (2 days)

  • Module 8
    • Introduction to Machine learning and different types of machine learning algorithms.

  • Module 9
    • Introduction to artificial neural networks (ANN): Includes learning about neurons as a computing element. Also, learn about Perceptron using the McCullogh-Pitts model.

  • Module 10
    • Financial data analysis and prediction; understanding dynamic pricing and revenue management; knowing about high dimensional data analysis.

Course Fees:

Total course fees is INR 7,40,250 + GST.

5. Data Science Specialization

Course Provider: John Hopkins University, through Coursera

Overview:

This online course is suitable for candidates familiar with Python and Regression concepts. It is a subscription-based model suitable for candidates comfortable with self-learning with limited or no personal support.

Teaching Mode: Online Learning

Curriculum:

There are 10 modules in this course.

  • Module-1: Data Science Tools

  • Module-2: R Programming

  • Module-3: Getting & Cleaning Data

  • Module-4: Exploratory Data Analysis

  • Module-5: Reproducible Research

  • Module-6: Inferential Statistics

  • Module-7: Regression Models

  • Module 8: Machine Learning

  • Module 9: Data Products

  • Module 10: Capstone Projects

Course Fees:

Approximately USD 800 for 11 months subscription (Please check for the latest fee).

The real questions are: 

  1. Will enrolling in full-time courses yield better results or opting for self-paced learning add more value?
  2. Also, is this the right way to become a data scientist?

Let’s see.

For the first question, enrolling in on-campus full-fledged courses is a good idea. But if you want to learn Data Science as a novice and are conscious about ROI, we will say opting for a professional certification course is better. You get to learn at your own pace.

For instance, if you are learning on AnalytixLabs, you get:

  • In-person Classroom, Live online, eLearning, and blended mode
  • Extensive and industry-relevant based on the latest trends
  • Taught by seasoned professionals
  • High value for money considering the duration, support, and industry reputation

Coming to the second question – Yes. Learning is the right way to go. A professional course is perfect if you are working and looking to change your career. If you have the bandwidth to take off from your job and do an on-campus course, that’s great too.

If you are a novice, follow our guide on How To Become a Data Scientist to get started asap.

And that’s a wrap.

We hope to have cleared all your doubts about getting started with Data Science Courses. If there is anything more you want to know, let me know in the comments section. All the best to you.

You may also like to read: Top 25 Data Science Books to Learn Data Science

Pritha leads all content marketing and communications efforts for AnalytixLabs. She is a communications and branding specialist and has an eye for detail. She believes it's a good thing to be a grammar Nazi; otherwise, she is a book buff. Most of the time she is seen trying to strike a balance between being a marketer, a home-maker and a mom!

2 Comments

  1. Thank you for sharing this valuable content, it is very informative and informal.

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