Become a Data Science Professional with IITM Pravartak Certification in Advanced Programming

This week you will go through the basics of competitive programming and data sciencew as well as well we will come across the current industry trends in Data Science

We will go through the basics of python with all essential beginner friendly concepts of python programming like datatypes, loops, data structures and functions, followed by assessments and assignments

Since we have the essential basics of python we will see some advanced concepts like Comprehensions, File handling, Regular Expressions, Object-oriented Programming, Pickling and many more essential concepts.

We will explore the need for Algorithmic Thinking and the necessity of efficient coding, we will drive through Data Structures and Algorithms along with Memory Management Techniques

Since we need to handle huge amounts of data, we will be implementing data handling techniques with Pandas library. And we will explore the different miscellaneous functions of Pandas library in detail.

We will drive into the SQL-based database. We will learn the basics of SQL queries, schemas and normalization.

we will continue into the SQL-based database. We will learn the SQL Advancedqueries,join,Date and Time Functions and SubQueries.

We will go through Probability and Statistics whereas they are key to understand, process and interpret the vast amount of data, we will deal with the basics of probability and statistics like Probability theory, Bayes theorem, distributions etc and their importance. Besides that, we will do hands-on with Numpy upon those concepts

Probability and Statistics with Numpy - Continued

We will continue with statistics and probability and we will deal with descriptive and inferential statistics along with Hypothesis testing and a lot of other relevant statistics methods

Data Visualization is used to understand data in a visual context so the patterns, trends and correlations in the data can be understood. We will do a lot of visualization with libraries like Seaborn, Matplotlib etc in turn that leads to effective storytelling.

It is always needed to analyze the data and preprocess it, since the real world data is not always industry ready, so in this week we will be dealing with a lot of data cleaning and Exploratory data Analysis techniques which is a very crucial stage for any data science project.

Real world data is always messy and it's very important to understand the statistical nature of data. Exploratory Data Analysis process, involving the preliminary examination of data to understand its characteristics, uncover patterns and identify potential insights.

We are going to explore the need of machine learning and its types, Algorithms when to use and how to use essential mathematical intuition along with Evaluation metrics. We will see in detail about regression algorithms.

In continuation to the ML algorithms we are going to see in detail about different classification algorithms along with mathematical intuition and evaluation metrics

We are going to explore classification algorithms like tree based algorithms in detail like how to interpret trees, pruning and ensemble methods like bragging and boosting etc

After dealing with a lot of Supervised machine learning algorithms we will compare and get to know when to use what, Besides that we will deal with the do’s and don'ts while training an ML model.

Now we will explore Unsupervised learning algorithms, why unsupervised ?, when to use it and as well as the essential mathematical intuition

As we move on to more complex problems, such as object recognition and text analysis, our data becomes extremely high dimensional, and the relationship becomes nonlinear. To accommodate this complexity, we move on to building more complex models that resemble our brain.

Now that we have a better theoretical understanding of deep learning models, we will spend this module implementing some of these algorithms in PyTorch

Now that we have the basic understanding of PyTorch, we will now dive into discussing the implementation details of a few state-of-the-art deep learning architectures in PyTorch

We are going to explore Natural Language Processing (NLP). Given the fact that we have a decent understanding of Machine Learning and Deep Learning, we can now explore the powerful ways to handle the NLP usecases

Having a basic understanding of NLP use cases, now we will dive into the CoPputer Vision FundaPentals. We will discuss state-of-the-art CV problePs and their solutions with deep learning.

Having a good understanding of ML, DL and various use cases, we will now discuss the platforms through which we can securely deploy these powerful models on production level. More specifically; we will discuss the fundamentals of AWS services and how to use them efficiently

This whole week we are going to work on industry projects which are currently in demand in the guidance of industry experts

Eventually, it's time to attend the mock interviews which will be conducted by the industry experts like Data scientists, IIT professors and renowned HR’s inorder to mould you in every area possible