# CoursesContents

- What is Data Science, what does a data scientist do
- Various examples of Data Science in the industries
- How Python is deployed for Data Science applications
- Various steps in Data Science process like data wrangling, data exploration and selecting the model
- Introduction to Python programming language
- Important Python features, how is Python different from other programming languages
- Python installation, Anaconda Python distribution for Windows, Linux and Mac
- How to run a sample Python script, Python IDE working mechanism?
- Running some Python basic commands
- Python variables, data types and keywords.

- Introduction to a basic construct in Python
- Understanding indentation like tabs and spaces
- Python built-in data types
- Basic operators in Python
- Loop and control statements like break, if, for, continue, else, range() and more.

- Central Tendency
- Variabiltiy
- Hypothesis Testing
- Anova
- Correlation
- Regression
- Probability Definitions and Notation
- Joint Probabilitiesn
- The Sum Rule, Conditional Probability, and the Product Rule
- Baye’s Theorem

- Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
- What are access modifiers, instances, class members
- Classes and objects
- Function parameter and return type functions
- Lambda expressions.

- Introduction to mathematical computing in Python
- What are arrays and matrices, array indexing, array math, Inspecting a numpy array, Numpy array manipulation

- Introduction to scipy, building on top of numpy
- What are the characteristics of scipy
- Various subpackages for scipy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more, Bayes
- Theorem with scipy.

- What is a data Manipulation. Using Pandas library
- Numpy dependency of Pandas library
- Series object in pandas
- Dataframe in Pandas
- Loading and handling data with Pandas
- How to merge data objects
- Concatenation and various types of joins on data objects, exploring dataset

- Introduction to Matplotlib
- Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more
- Matplotlib API

- Revision of topics in Python (Pandas, Matplotlib, numpy, scikit-Learn)
- Introduction to machine learning
- Need of Machine learning
- Types of machine learning and workflow of Machine Learning
- Uses Cases in Machine Learning, its various arlogrithms
- What is supervised learning
- What is Unsupervised Learning

- What is linear regression?
- Step by step calculation of Linear Regression
- Linear regression in Python
- Logistic Regression
- What is classification
- Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier (Self-paced), Support Vector
- Machine(self-paced), xgboost (self-paced)

- Introduction to unsupervised learning
- Use cases of unsupervised learning
- What is clustering
- Types of clustering(self-paced)-Exclusive clustering, Overlapping Clustering, Hierarchical
- Clustering(self-paced)
- What is K-means clustering
- Step by step calculation of k-means algorithm
- Association Rule Mining(self-paced), Market Basket Analysis(self-paced), Measures in association rule
- mining(self-paced)-support, confidence, lift
- Apriori Algorithm

- Introduction to pyspark
- Who uses pyspark, need of spark with python
- Pyspark installation
- Pyspark fundamentals
- Advantage over mapreduce, pyspark
- Use-cases pyspark and demo

- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- LDA

- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACFl