Introduction to Linear Algebra, Matrices Operations, Introduction to Statistics, Measures of central tendencies, Measures of variance, Measures of frequency, What are analytics and data Science?, Business Analytics vs. Data Analytics vs. Data Science, Common Terms in Analytics, Analytics vs. Data warehousing, OLAP, MIS Reporting, Types of data (Structured vs. Unstructured vs. Semi-Structured), Relevance of Analytics in industry and the need of the hour, Critical success drivers, Overview of analytics tools & their popularity, Analytics Methodology & problem-solving framework, Stages of Analytics.

Python getting Started, Variables, Datatypes, Strings, Operators, Lists, Tuples, Sets, Dictionaries, Python Conditions and If statements, Looping concept, Functions, Lambda, File handling.

1. Descriptive Statistics – Mean, Median, Mode.
2. Variability – Standard deviation, Variance, Range, Percentile,Quartile,Interquartile Range.
3. Correlation.
4. Probability Distribution.
6. Normal Distribution.

Exploratory Data Analysis with Python, Pandas and NumPy, Data Visualisation in Python (Matplotlib/Seaborn/Plotly), Data Engineering with Python.

Linear Algebra: Vectors, Matrices, Norms, Subspaces, Projections, SVD, EVD, Derivatives of Matrices, Vector Derivative, Identities, Least Squares, Probability and Statistics with NumPy, Probability theor0079, Bayes theorem, Pearson Distribution, Spearman Distribution, Advanced Linear Models : Least Squares, Bias, Statistical Linear Models – Linear regression, Logistic regression.

• Machine Learning with Sklearn introduction
• Supervised Machine Learning
  • Linear Regression (Univariate, multilinear)
  • Polynomial Regression
  • Logistic Regression
  • Naïve bayes
  • Support Vector
  • Decision tree
  • Random forest
  • KNN
• Unsupervised Machine Learning
  • Clustering in machine learning
  • K means

Understand PyTorch and Deep Learning concepts, Perform basic operations on your dataset using tensors and variables, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, Extend your knowledge of Deep Learning by using PyTorch to solve your own machine learning problems, Build your neural network using Deep Learning techniques in PyTorch.

Implement sentiment analysis, complete analogies & translate words, Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words, Use recurrent neural networks, LSTMs, GRUs & Siamese networks in TensorFlow for sentiment analysis, text generation & named entity recognition, Syntax and Parsing.