Module 1. Python for Data Science


Fundamentals of Python
  • Advantages of Python
  • Python Compiler and PVM
  • Python installation and environment

Datatypes in Python
  • strings
  • char
  • lists
  • tuples
  • range
  • sets
  • dictionaries

Operators in Python

Input / Output

Control Statements
  • if statement
  • if...else statement
  • if...elif...else statement
  • while loop
  • for loop
  • break statement
  • continue statement
  • pass statement

numpy arrays
  • Array creation
  • Array attributes
  • 1D and 2D
  • matrices

Functions in Python
  • Built in and User defined functions
  • Writing our own functions
  • Importing functions

Modules and Packages
  • Creating our own modules and packages
  • Importing our modules and packages

Data Analysis in Python using pandas
  • Series
  • Dataframes
  • Creation of Dataframes from different sources
  • Viewing data in Dataframe
  • Operations on Dataframe
  • Handling missing data

Data visualization using matplotlib
  • Line plot
  • Bar graph
  • Pie chart
  • Sub plots
  • Histogram

Data visualization using seaborn
  • Distribution plot
  • Kde plot
  • Countplot
  • Box plot
  • Scatter plot
  • Sub plots
  • Lmplot
  • Pair plots

Module 2. Statistical Methods


Introduction to Statistics
  • What is statistics?
  • ypes of statistics
  • Descriptive statistics
  • Inferential statistics

Statistical terms
  • Population
  • Sample
  • Variable (discrete and continuous)
  • Data and types of data
  • Qualitative (nominal and ordinal)
  • Quantitative (interval scale and ratio scale)

Measures of Central tendency
  • Mean
  • Median
  • Mode

Probability
  • Probability with replacement
  • Probability without replacement
  • Probability Mass Function (PMF)
  • Probability Density Function (PDF)

Measures of Shape
  • Skewness
  • Kurtosis

Measures of Dispersion or Variability
  • variance
  • std
  • percentile
  • quartile
  • range
  • IQR

Application of Variance or Std
  • Empirical Rule
  • Problems on Empirical Rule
  • Chebyshev’s Theorem

Probability Distributions
  • Normal distribution
  • Standard normal distribution
  • Sampling distribution of Sample means
  • Central limit theorem
  • T- Distribution
  • Student T- Test
  • Chi Square Test (Goodness of Fit)
  • Binomial distribution
  • Bernoulli distribution
  • Geometric distribution
  • Hypergeometric distribution
  • Poisson distribution

Hypothesis Testing
  • Upper tail test
  • Lower tail test
  • Two tail test

ANOVA
  • One way ANOVA
  • Two way ANOVA

Module 3. Tableau


    Introduction to Tableau
    • Tableau tools
    • Datatypes in Tableau
    • Viewing data

    Creating Pivot table

    Data Blending

    Cross Database Joins

    Calculations on Data
    • Aggregate functions
    • Calculated fields

    Data visualizations in Tableau
    • Symbol maps
    • Bar chart
    • Stacked bar chart
    • Line chart
    • Pareto chart
    • Heat map
    • Pie chart
    • Scatter plot
    • Area chart
    • Dual Axis chart
    • Histogram
    • Bubble chart

    Dash board creation

    Module 4. Machine Learning in AI


    Exploratory data analysis (EDA)

    Outliers and their treatment

    Supervised Learning vs Unsupervised Learning

    Feature extraction and conversion
    • One hot encoding using dummy variables
    • One hot encoding using One hot encoder

    Regression Models
    • Simple Linear regression
    • Multiple Linear regression
    • Polynomial Linear regression
    • Ridge regression
    • Bias and Variance tradeoff
    • Lasso regression
    • ElasticNet regression

    Classification Models
    • Logistic regression
    • Naïve Bayes (Gaussian NB and Multinomial NB)
    • KNN Classifier
    • SVM
    • Regularization
    • Kernel Trick
    • Decision Tree
    • Entropy
    • Gini Index
    • Random forest
    • Confusion Matrix
    • Bootstrapping, Bagging and Boosting

    Unsupervised Learning
    • K-Means clustering
    • Elbow technique

    Association Rule Learning
    • Apriori Algorithm

    Model selection
    • Selecting appropriate model for our data

    Module 5. Deep Learning in AI


    Introduction to Deep Learning
    • Biological Neural Network
    • Artificial Neural Network
    • Perceptrons
    • Layers of a Network

    Activation functions
    • Identity function
    • Binary step function or Threshold function
    • Logistic function or Sigmoid function
    • ReLU function
    • Hyperbolic Tangent function
    • Softmax

    Creating Neural Network in Python
    • ANN
    • ANN with Activation functions

    TensorFlow and Keras
    • Variables
    • Constants
    • Placeholders
    • Graph / Tensor / Session
    • Tensorflow programming

    ANN in Tensorflow and Keras

    Convolutional Neural Network

    Recurrent Neural Network

    Module 6. Natural Language Processing in AI


    NLP Concepts
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop words
    • POS

    Feature Extraction
    • CountVectorizer
    • TfidfVectorizer

    Text Classification using NLP

    Module 7. Computer Vision in AI


    Object detection by Computer

    Course Highlights


    • 100+ industry oriented tasks are solved that are actually needed by a Data Scientist
    • Softcopy of the class notes
    • Datasets with code
    • Project with documentation
    • Resume preparation
    • All Interview Questions are discussed in the classes
    • Softcopy of Interview Questions
    • Placement Assistance