Data Science Course Overview

Data Science Course Overview

The typical data scientist of today does not start their journey like an engineer, doctor, lawyer or other professionals.
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The typical Data Scientist of today does not start their journey like an engineer, doctor, lawyer, or other professional. While the latter whet their expertise in dedicated formal degree courses, for a Data Science enthusiast, the journey is quite a labyrinth on the web. Countless hours could be spent in front of a screen, watching richly-animated lectures on the so-called top online learning platforms without optimal retention, which can be attributed to a host of factors.

For example, think of learning to drive a car (no, not self-driving cars). It’d only help so much to observe your peers, parents, and strangers without getting your hands on a steering wheel. Similarly, think about an imaginary author who envisages writing the world’s best book but does not feel it’s the “right time” to start unless he has known all the classics and writing styles inside-out. This plagues budding data scientists who do not learn and apply hands-on what they learn.

Pickl.Ai, with the aid of its experienced in-house experts, who have spent their formative years in an aberrative and up-and-coming industry whilst the market has been in the doldrums, has built a course tailored to these requirements. Their intimacy with the demands of the Indian ecosystem, along with tacit knowledge, holds them in good stead to address the apprehensions of the students of today.

All our courses have the one-same syllabus, which follows a bottom-to-top approach so that the subject matter is exposited and learned gradually. We have rigorously ensured that no conceptual gaps are introduced, which is often cited as a reason for discontent and dissatisfaction in the era of MOOCs. Following are the modules of the course structure:

Data Science Course Overview – Pickl

Python for Data Science

Python is arguably the most-adopted programming language by Data Scientists, owing to the amount of flexibility and functionality it offers through its libraries. Apart from the introduction and setup of Jupyter Notebook, the module has three main sub-modules:

Introduction: It focuses on basic programming principles, with topics like data types, variables, conditionals, and functions in focus. The Pythonic syntax is addressed as well.

Data Structures: Lists, dictionaries, and sets are taken up, along with iterative constructs. This acts as an intermediary for learners who can now boast of knowing the sheer basics well enough.

Libraries: Matplotlib, a powerful library for visualization, and NumPy, suited for a broad range of numeric applications, are elucidated here. These form the building blocks for the upcoming sections.

The section is strewn with quizzes for assessing the understanding of the learners. Notebooks used in the lectures are provided for download, along with programming assignments.


Pandas is a software library that is suited for data manipulation and analysis. It is considered to be an indispensable resource for professionals, owing to its simple yet powerful syntax, which bolsters resourcefulness while dealing with large datasets. Various popular courses leave out this section, which affects the learner negatively, whenever an unseen line of code pops up as a step of a larger problem. Thus, ten lectures have been devoted to illustrate and enunciate the nuances of the package. The section concludes with a quiz and an in-depth programming assignment.

Introduction to Statistics

Statistics is the bedrock of data science simply because its concepts are intertwined with all major paradigms of Data Science. Measures of central tendency, spread, position, histograms, types of distributions (with a stress on the normal distribution and Student’s-T distribution), central limit theorem, and hypothesis testing are explained.

Every lecture in this module is followed by a quiz, owing to the theoretical nature of the subject. This is complemented by the final quiz and the assignment, which reinforce the learnings accumulated. Thus, the learner is set to dive deep into the advanced concepts.

Introduction to machine learning

All the relevant buzzwords – learning process, Exploratory Data Analysis (EDA), feature selection, scaling and engineering, performance, and bias-variance – are engaged in, with an emphasis on building the right intuition. Contrary to popular perception, this is where data scientist spends most of their time. Thus, the philosophy and idea behind ‘the preparatory stage’ are communicated. There are five quizzes and two assignments, to reinvigorate the fundamentals.

Supervised Learning-I

Having understood the rudiments, this section introduces and trains learners in the two most widely-adapted Machine Learning techniques: linear regression and logistic regression. There are separate videos on model training and evaluation for both of these, so that the conventional flow of action during a real-world analysis is understood. The assumptions, as well as the variations, have been included as well so that a sturdy comprehension is ensured.

Supervised Learning-II

The module discusses additional concepts like decision trees, classification trees, bagging, and boosting, to equip prospective practitioners with the knowledge of other tools that are employed for achieving different end goals. This complement and based upon the previous sections and cap off the two-part module on supervised learning

Unsupervised Learning

Clustering is taken up in this section. K-Means clustering, hierarchical clustering, association rule mining, and recommendation systems form the body of the subject matter covered. This section, along with the last one, is short on quizzes but those shall be offered in due course of time.

In addition to the curriculum stated above, you also get to be a part of an interactive and vibrant learning community, which will supplement your learning pursuit. Doubts posted are addressed by instructors and peers alike. The learning portal is dynamic and allows you to learn anywhere and anytime at your convenience. While making sure that the communication isn’t one-way, we also believe in continuing our ties with our learners even after the course has been finished.

Our course has multiple variants. While Apprentice goes beyond the run-of-the-mill courses with live classes and case discussions, Wizard goes further by providing tips on job preparation in the form of CV-review and mock interviews and guidance on an industry-pertinent project. These are pinpoint and concise in nature, to ensure you continue to advance fruitfully in your professional aspirations. Please note that the assignments indicated above are not provided for the Free and Dabbler variants.

All in all, we would welcome all Data Science enthusiasts to come join us in changing the way we perceive learning Data Science online. Take your time before taking the decision. Be rest assured though that your investment in your future is the best investment you can make and with our assistance, no stone would be left unturned to align it with your vision.

Read Blog? Data Science Mindset


  • Ayush Pareek

    Written by:

    I am a programmer, who loves all things code. I have been writing about data science and other allied disciplines like machine learning and artificial intelligence ever since June 2021. You can check out my articles at I have been doing my undergrad in engineering at Jadavpur University since 2019. When not debugging issues, I can be found reading articles online that concern history, languages, and economics, among other topics. I can be reached on LinkedIn and via my email.