Become an IIT Certified Programming Professional & Master 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.

Now we are going to explore the popular NoSQL databases like MongoDB and their importance as well as their key components. We will perform several operations like CRUD operations etc.

We will continue with MongoDB beyond basics like embed documents, dealing with types of data, schema types and Data relationships in MongoDB etc

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.

Plotly’s Dash is a famous open-source data visualization library which we will be using to build custom data visualization projects which allows for better 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 will not always be of the numerical form, we need to know how to handle a lot of other forms of data like image and text. In this week we are going to see all essential data analysis techniques on image and text data.

We are going to explore the need for 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 them and as well as the essential mathematical intuition

We are going to explore Natural Language Processing (NLP). At first, we will see Syntactic analysis or parsing to analyze text using basic grammar rules to identify sentence structure, how words are organized, and how words relate to each other. Some of them are Tokenization, Part of Speech Tagging (PoS Tagging), Lemmatization and Stemming, Stop word removal etc

Now we will see that Semantic analysis focuses on capturing the meaning of the text. First, it studies the meaning of each individual word (lexical semantics). Then, it looks at the combination of words and what they mean in context. Some of the sub-tasks of semantic analysis which we are going to explore are Word sense disambiguation, Relationship extraction. Besides that, we will explore Sentiment Analysis, Text extraction etc

This whole week we are going to work on industry projects which are currently in demand under 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 in order to mould you in every area possible