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Summary: Pickl.AI’s Data Science course provides a structured, hands-on approach to learning essential topics like Python, Pandas, statistics, and machine learning. With quizzes, assignments, and career-focused variants, the course ensures a thorough understanding and readiness for the Data Science industry, tailored to individual professional goals.
Introduction
The typical Data Scientist of today does not start their journey like an engineer, doctor, lawyer, or other professional. While the latter had expertise in dedicated formal degree courses, for a Data Science enthusiast, the journey was quite a labyrinth on the web.
You could spend countless hours watching richly animated lectures on top online learning platforms, but without optimal retention due to various 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 while 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 today’s students.
All our courses follow a bottom-to-top syllabus, presenting and teaching the subject matter gradually. We rigorously ensure that our approach avoids conceptual gaps, which often lead to discontent and dissatisfaction in the era of MOOCs.
Following are the modules of the course structure:
Python for Data Science
Python is arguably the most-adopted programming language by data scientists, owing to the flexibility and functionality it offers in its libraries. Apart from the introduction and setup of Jupyter Notebook, the module has three main sub-modules:
- Introduction: This chapter focuses on basic programming principles, including topics like data types, variables, conditionals, and functions. It also addresses the Pythonic syntax.
- Data Structures: Lists, dictionaries, sets, and iterative constructs are covered. This acts as an intermediary for learners who can now boast of knowing the basics well enough.
- Libraries: Matplotlib, a powerful visualisation library, and NumPy, suited for various numeric applications, are elucidated here. These form the building blocks for the upcoming sections.
The section is strewn with quizzes to assess the learners’ understanding. Notebooks used in the lectures and programming assignments are also provided for download.
Pandas
Pandas is a software library for data manipulation and analysis. Professionals find it indispensable due to its simple yet powerful syntax, which enhances their ability to handle large datasets effectively.
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. We have dedicated ten lectures to illustrating and explaining the nuances of the package. The section concludes with a quiz and an in-depth programming assignment.
Introduction to Statistics
Statistics forms the foundation of data science because its concepts underpin all major paradigms in the field. We explain measures of central tendency, spread, position, histograms, and types of distributions, with a focus on the normal distribution and Student’s-T distribution. Additionally, we cover the central limit theorem and hypothesis testing.
Because the subject is theoretical, we follow each lecture in this module with a quiz. The final quiz and the assignment complement this and reinforce the learning 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, emphasising building the right intuition. Contrary to popular perception, this is where data scientists spend 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
Supervised learning is a machine learning technique that trains a model on labelled data. The model learns to map input data to the correct output by analysing examples. It then uses this learned mapping to predict outcomes for new, unseen data, making it highly effective for tasks like classification and regression.
After understanding the rudiments, this section introduces and trains learners in the two most widely adapted Machine Learning techniques: linear regression and logistic regression.
We provide separate videos on model training and evaluation for both topics to help you understand the conventional flow of action during real-world analysis. We include assumptions and variations to ensure a thorough comprehension.
Supervised Learning-II
The module discusses additional concepts like decision trees, classification trees, bagging, and boosting to equip prospective practitioners with other tools for achieving different end goals. This complements and based upon the previous sections and caps off the two-part module on supervised learning
A decision tree is a flowchart-like model that helps you make decisions and predictions. Each branch represents a decision rule, and each leaf node represents an outcome. It’s used for classification (categorising data) and regression (predicting values).
Classification Trees are decision trees specifically used for classification tasks. The goal is to assign data into different categories based on input features. Each path from the root to a leaf represents a series of decisions that lead to a final classification.
Bagging (Bootstrap Aggregating) is an ensemble learning method that improves the accuracy of machine learning models. It involves training multiple versions of a model on different subsets of the data and then averaging their predictions. This reduces variance and helps prevent overfitting.
Boosting is another ensemble technique that combines multiple weak models to create a strong one. Unlike bagging, increasing focus on correcting errors made by previous models gradually improves the overall prediction accuracy. It’s particularly effective in reducing bias.
Unsupervised Learning
This section covers clustering, including K-Means clustering, hierarchical clustering, association rule mining, and recommendation systems. This section, like the last one, is short on quizzes, but those will be offered eventually.
In addition to the curriculum stated above, you will be a part of an interactive and vibrant learning community, which will supplement your learning pursuits. Instructors and peers alike address posted doubts.
The learning portal is dynamic and allows you to learn anywhere and anytime at your convenience. We ensure that communication remains interactive and maintain our connection with learners even after they finish the course.
Our course has multiple variants. While Apprentice goes beyond the run-of-the-mill courses with live classes and case discussions, Wizard provides tips on job preparation through CV review, mock interviews, and guidance on an industry-relevant project.
These are pinpoint and concise to ensure you continue to advance fruitfully in your professional aspirations. Please note that the assignments above are not provided for the Free and Dabbler variants.
All in all, we would welcome all Data Science enthusiasts to join us in changing the way we perceive learning Data Science online. Take your time before making the decision. Rest assured that investing in your future is the best decision you can make. With our assistance, we will leave no stone unturned to align it with your vision.
Read Blog: Data Science Mindset
Frequently Asked Questions
What makes Pickl.AI’s Data Science course unique?
Pickl.AI’s Data Science course stands out by offering a structured, hands-on approach that ensures no conceptual gaps. Industry experts with deep knowledge of the Indian market designed this course. It includes practical assignments and quizzes to reinforce learning effectively.
How does Pickl.AI’s course structure benefit Data Science learners?
Pickl.AI’s course structure follows a bottom-to-top approach, gradually exposing learners to essential concepts. This ensures a smooth learning curve, avoiding the confusion often seen in other online courses, and enables better retention through practical exercises and assessments.
What additional benefits do Pickl.AI’s paid courses offer?
Paid versions of Pickl.AI’s courses, such as Apprentice and Wizard, offer live classes, case discussions, CV reviews, mock interviews, and project guidance. These benefits enhance career readiness and ensure learners are well-prepared for the Data Science industry.
Conclusion
Pickl.AI’s Data Science course offers a comprehensive, hands-on learning experience tailored to the needs of aspiring data scientists. With a structured curriculum covering essential topics like Python, Pandas, statistics, and machine learning, the course ensures no conceptual gaps. Learners benefit from quizzes, assignments, and interactive community support.
The course variants provide additional career-focused benefits, making Pickl.AI a valuable investment for anyone serious about a career in Data Science. Whether you’re just starting out or aiming to advance your skills, Pickl.AI’s courses will align with your professional goals.
Authors
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Written by:
Aashi VermaReviewed by:
