DATA SCIENCE INTRODUCTION TOPICS Data Science Machine Learning. Data Mining. Deep Learning. Artificial Intelligence. Descriptive Analysis. Predictive Analysis. Python PYTHON BASICS REQUIRED FOR DATA SCIENCE AND MACHINE LEARNING Basics Python. Types of Variables Numbers Strings Lists Dictionaries Tuples Statements Looping Function Database Connectivity in Python Installation of Anaconda Distribution Working Framework Jupyter Notebook PYTHON LIBRARIES Introduction to Libraries Required for Data Science NumPy Pandas MatplotLib SciPy Seaborn NUMPY NdArray In NumPy ndim, shape, size, dtype, itemsize, data Array Creation Printing Arrays Basic Operations Universal Functions Indexing, Slicing and Iterating   Shape Manipulation Using NumPy Changing the shape of an array Stacking together different arrays Splitting one array into several smaller ones Copies and Views In NumPy No Copy at All View or Shallow Copy Deep Copy   Fancy indexing and index tricks Indexing with Arrays of Indices Indexing with Boolean Arrays The ix_() function Linear Algebra In NumPy Simple Array Operations   Tricks and Tips Automatic Reshaping Vector Stacking PANDAS Pandas Basics Object Creation Viewing Data Selection Missing Data Operations Merge Grouping Reshaping   Essential Basic Functionality Head and Tail Attributes and Underlying Data Accelerated operations Flexible binary operations Descriptive statistics Function application Reindexing and altering labels Iteration .dt accessor Vectorized string methods Sorting Copying dtypes Selecting columns based on dtype Intro to Data Structures Series DataFrame Panel MATPLOTLIB Introduction of Matplot Library Intermediate Advanced Colors Text Toolkits SCIPY Introduction Of SciPy Library Basic functions Special functions Integration Optimization Interpolation Signal Processing Linear Algebra Spatial data structures and algorithms Statistics SEABORN Plotting functions Visualizing statistical relationships Relating variables with scatter plots Emphasizing continuity with line plots Showing multiple relationships with facets   Plotting with categorical data Categorical scatterplots Distributions of observations within categories Statistical estimation within categories Plotting “wide-form” data Showing multiple relationships with facets Visualizing the distribution of a dataset Plotting univariate distributions Plotting bivariate distributions Visualizing pairwise relationships in a dataset   Visualizing linear relationships Functions to draw linear regression models Fitting different kinds of models Conditioning on other variables Controlling the size and shape of the plot Plotting a regression in other contexts Multi-plot grids Building structured multi-plot grids Conditional small multiples Using custom functions Plotting pairwise data relationships   Plot aesthetics Controlling figure aesthetics Seaborn figure styles Removing axes spines Temporarily setting figure style Overriding elements of the seaborn styles Scaling plot elements Choosing color palettes Building color palettes Qualitative color palettes Sequential color palettes Diverging color palettes Setting the default color palette DATA PREPROCESSING AND DATA ANALYSIS Data Cleaning or Data Cleansing. Data Integration. Data Transformation. Data Reduction. Data Discretisation. Data Visualisation. Feature Scaling MYSQL DATABASE Introduction to Database Data Types SQL Operators SQL Functions SQL DDL Commands Create database Drop database Create table Drop table Alter table  SQL DML Commands Select Where Group By Order By    Joining In SQL Inner Join Left Outer Join Right Outer Join Full Outer Join Examples STATISTICS Types Of Statistics Descriptive Statistics. Mean, Median, Mode, Variance etc.   Inferential Statistics. Linear Regression. Binomial Distribution. Normal Distribution. Chi-Squared Test. Probability. Permutation and Combination. Least Square etc. DATA STRUCTURES Algorithms. Aysymptotic Notations Greedy Algorithm Divide And Conquer Pointer Time Complexity Array   Linked Lists. Linked Lists. Doubly Linked Lists. Circular Linked Lists. Circular Doubly Linked Lists. Stack and Queue. Stack Implementation Queue Implementation   Searching Techniques. Linear Search Binary Search Interpolation Search Hash Table Sorting Techniques Insertion Sort Selection Sort Bubble Sort Quick Sort Merge Sort Heap Sort   Graph. Graph Traversal Algorithm Depth First Search ( DFS ) Breadth First Search ( BFS )   Tree. Tree Traversal Binary Search Tree AVL Tree Spanning Tree B Tree B+ Tree Recursion. Tower Of Hanoi Fibonacci Seres TYPES OF MACHINE LEARNING Supervised Learning Unsupervised Learning Reinforcement Learning SUPERVISED LEARNING Factors in Machine Learning What is Dependent variable ? What is Independent variable ? What is Sample And Population ? Least squares Regularization Correlation. Training Data and Test Data. Cross Validation. Type I and type II error. Interpolation and Extrapolation. False Positive and False Negative. Bias and Variance. File Format. Outliers Underfitting and Overfitting Dimensionality Reduction Regression Algorithms. Linear Regression With One Variable. Gradient Descent. Cost Function. Hypothesis function Linear Regression With Multiple Variable. Polynomial Regression. Logistic Regression. Decision Tree Regression. Random Forest Regression.   Classification Algorithms. Support Vector Machine (SVM). Hyper Planes Support Vectors Small margin Large margin   Time Series Forecasting. Trends Linear Trend and Non Linear Trend Seasonal Trend Cyclical Trend Irregular Trend Autoregressive ( AR ) Moving Average ( MA ) Autoregressive Moving Average ( ARMA ) Autoregressive Integrated Moving Average ( ARIMA ) Naive Bayes. K Nearest Neighbours. UNSUPERVISED LEARNING What is Clustering ? Why Clustering ? Applications of Clustering Types of Clustering Algorithms K means Clustering. Hierarchical Clustering. Mean Shift Clustering.