In this part we will be covering all the basic and advance topics that are needed to get started with Data Structures and Algorithms. We will First start with C++ from Scratch and then proceed with the topic below. Every day we will be giving you guys 5 Competitive Programming Questions for preparing you for the interviews. And everyday we will be discussing the answers for all the 5 questions from the previous day.

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• Introduction
• Mathematics
• BitManipulation
• Recursion
• Arrays
• Searching
• Sorting
• Matrix
• Hashing
• Strings
• Linked Lists
• Stack
• Queue
• Deque
• Trees and BST
• Heap
• Graph
• Greedy Algorithms
• BackTracking
• Dynamic Programming
• Trie
• Segment and Binary Indexed Trees
• Disjoint Sets

In this part we will be covering all the basic and advance topics that are needed to get started with Data Analysis.

• Data Types and Operators
• Control Flow
• Scripting
• Object Oriented Programming in Python

Go through the entire data analysis process, starting by posing a question and finishing by sharing your findings.

• Anaconda and Jupyter Notebooks
• Data Analysis Process
• Numpy and Pandas ( in-depth )
• Matplotlib and Seaborn ( in-depth )
• ‍CAPSTONE PROJECT: Investing a Dataset

In this part we will be covering the use of descriptive statistics. This will give the readers a good intuition for the data and use of statistical inference to draw a conclusion based on the results.

• Learn about constructs, population vs sample, correlation vs causation, hypotheses, and experimentation.
• Visualising Data, Central Tendency, Variability, Standardizing.
• Normal and Sampling Distribution.
• Estimation, Hypothesis Testing.
• t-tests and much more.

Master SQL, the core language for Big Data analysis, and enable insight-driven decision-making and strategy for your business.

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• Basic SQL, SQL Joins, Aggregations, Subqueries & Temporary Tables, SQL Data Cleaning, Window Functions.
• SQL Advanced JOINs & Performance Tuning
• Accessing database using python
• CAPSTONE PROJECT: Wrangle OpenStreetMap Data

Learn the data wrangling process of gathering, assessing, and cleaning data. Learn how to use Python to wrangle data programmatically and prepare it for deeper analysis.

• Gathering Data
• Assessing Data
• Cleaning Data
• Working with Tabular, CSV, Excel and JSON data
• Learn about HTML and XML
• Use BeautifulSoup to scrape web pages.
• Data Quality

We will learn the mathematics, intuition and code all the machine learning algorithms.

• Simple, Multiple and Polynomial  Linear Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Logistic Regression
• K-Nearest Neighbors
• Support Vector Machine (SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• K-Means Clustering
• Hierarchical Clustering
• Apriori
• Eclat
• Upper Confidence Bound(UCB)
• Thompson Sampling
• Natural Language Processing (NLP)

We will learn the mathematics, intuition and code all the deep learning algorithms. We will introduce Keras, Tensorflow, Pytorch

• Artificial  Neural Nets ( ANN )
• Convolutional Neural Nets ( CNN )
• Recurrent Neural Nets ( RNN )
• Self Organizing Maps ( SOMs )
• Boltzmann MAchine
• Autoencoders and GANs
• Natural Language Processing (NLP)