What is Data Science with Python?

• What is Data Science?
• What is Big Data?
• What is ML?
• What is Analytics?
• What is Data analysis and Data Mining?
• Analytics project life cycle
• Real life applications, projects and
  career paths of Data Science

• Definition and Computation of Probability
• Sampling and Sampling distributions
• Prediction and Confidence intervalComputation and Analysis
• Measurement of central tendencies and it’s applications
• Spreads, Distributions(Normal, Z-distribution, Binomial, Poisson) and
  various types of probability distributions(Continuous and discrete)
• Measures of shape( Skewness and Kurtosis)
• Measures of relationship between variables(Correlation, causation)
• Hypothesis Testing(t-test, Chi-square, Anova)
• Measures of Dispersion (Variance, Std. deviation, Range)
• Missing Value theorem
• Contigency Table

• What is EDA and why is it required?
• Outlier treatment
• Data distributions and transformations
• Graphs
• Bar charts
• Histogram
• Box-Whisker plot
• Scatter plot
• Variable selection
• Bubble charts
• Exam

• What is Python?
• History
• Why is Python preferred for
  Data Science?
• Installation of I python/Jupyter
  Notebook/ SPYDER

• Keywords
• Built-in functions
• String Formatting
• Lists
• Loops
• Tuples
• Indexing
• Slicing
• Sequences
• Dictionaries
• Sets
• Importing and exporting data from
  python into various formats

• Errors in Python
• Abnormal termination
• Exception handling methods
• Ignoring Errors
• Assertions and effective
  usage of assertions

• User defined functions
• Parameters
• Nested functions
• Local and Global variables
• Alternate Keys
• Lambda functions
• Sorting Lists and Dictionaries
• Sorting Collections

• Methods and Inheritance
• Abstraction and Encapsulation
• Classes
• Walking Directory Trees
• Initializes
• Instance methods
• Class methods
• Data Static Methods
• Expressions
• Module Aliases
• Math functions
• Random Numbers
• Package Installation Methods
• Introduction to Numpy, Pandas
  and other libraries
• Plotting in Python
• Creating Data Frames
• Data Manipulation
• Slicing and Dicing

Machine Learning

Crisp-DM Process

Supervised Learning:

• What is supervised learning
• Algorithms in Supervised learning
• Steps in Supervised learning

• Regression vs classification
• Computation of co-relation coefficient
  and Analysis
• Performance and accuracy measurement
  of a Model
• Naive Baye’s classifier
• Model Training, Validation and Testing
• Ordinary Least squares
• Variable selection
• R-Square coefficient and RMSE as a
  strength of model
• Prediction and confidence interval
• Detrmnston Proviso of Regression and aplication
• Proviso of Regression
• Dummy variables
• Types of Regression: Linear and Logistic
  (Simple and multiple)
• Sum of least squares
• ROC and AUC curves
• Homoscedasticity and Heteroscedasticity
• Multicollinearity and vif
• Confusion matrix
• Techniques to improve accuracy and
  performance of regression models
• Assignment

• Introduction to Decision tree Algorithms and
  it’s applications
• Classification and regression trees-CART
  models, ID3,C4.5
• CHAID analysis
• Building Decision Trees using R
• Decision nodes and leaf nodes
• Variable Selection, Parent and child
  nodes branching
• Stopping Criterion
• Tree pruning
• Depth of a tree
• Overfitting
• Metrics for decision trees-Gini impurity,
  Information Gain, Variance Reduction
  Regression using decision tree
• Interpretation of a decision tree using If-else
• Pros and cons of a decision tree
• Introduction to Random forest test and
  it’s applications
• Why Random forest test?
• Tree bagging
• Models and algorithms in Random Forest test
• Training Data set, Tree grouping and decision
  making on majority voting
• Boosting algorithms-Gradient Boosting,
  Adaptive boosting-Adaboost,
  Xgboost (Advanced)
• Accuracy estimation using cross validation

• What is KNN and why do we use it?
• KNN-algorithm and regression
• Curse of dimensionality and brief introduction
  to dimension reduction
• KNN-outlier treatment and anomaly detection
• Cross Validation
• Pros and cons of KNN

• Linear and Non-Linear SVM’s
• SVM regression
• Train time and Run time complexities
• Kernel Methods

• What is unsupervised learning?
• Alogrithms in unsupervised learning
• Steps in unsupervised learning

• Introduction to dimensionality reduction
  and it’s necessity
• Principal Component Analysis (PCA)
• Singular Value Decomposition(SVD)
• Kernel-PCA
• Linear Discriminate Analysis
• Feature extraction
• Advantages and applications of
  Dimensionality reduction

• Introduction to clustering
• Real-life applications of clustering
• Distance measurement methods
• Hierarchical clustering
• K-Means clustering and skew plot
• Assignment

• Introduction to Text Mining
• Applications
• Structured and unstructured data
• Extracting unstructured text from
  files and websites
• Data cleaning and reshaping
• Terminologies in Text Mining
• Text clustering and categorization
• Word cloud
• N-gram charts
• Sentiment Analysis
• Twitter Analytics
• Natural Language processing
• Assignment

• Introduction to forecasting
• Applications
• Data Manipulation and Cleaning
• Time Series
• Time Series forecasting
• Components of Time Series-Trend,
  Seasonality, Randomness
• Trend Analysis
• Forecasting methods
• Smoothing Methods
• Modeling Random Components
• Modeling forstationary time series
• ETS Model
• Auto regressive Model
• Moving Average Model
• ARIMA Model
• Anomaly Detection
• Transformations
• Growth curve
• ARCH & GARCH Models

• Introduction
• Importance of Association rules
• Metrics of rules-Lift, Support,
  Confidence, Conviction
• Apriority Model
• Market Basket Analysis
• Algorithm implementation and tuning
• Applications
• Assignment

Introduction to Neural Networks

• Introduction to NN
• Basic understanding of ANN
• Basic understanding of CNN
• Basic understanding of RNN
• Basic understanding of Back propagation
• Basic understanding of Feed Forward
• Perceptron, Multi-Layer Perceptron
• Structures
• Activation Functions
• Optimizers
• Loss Function
• Cost function
• Computer vision
• LSTM, GNU, DCNN, Marchov Chain, Boltzmann Machine

• Introduction to NLP
• Text Preprocessing
• Feature Extraction
• Named Entity Recognition
• Sentiment Analysis
• Text Classification
• Word Embeddings
• Speech Recognition
• Chatbots & Virtual Assistants
• Deep Learning for NLP
• Language Translation
• Topic Modeling

• 10 real time projects with data sets from different sectors like Healthcare
  sector, banking& finance sector,real estate sector…
• course completion certificate
• Internship certificate