Data Science

Data Science

Data Science is using various tools and systems to analyze and interpret the data and its patterns. It includes Statistics, Data Science, Python, R, Apache Spark & Scala, Tensorflow, Machine Learning, Deep Learning and Tableau.
  • Foundations of programming:
    Python built-in Data types
    • Understanding Data Types in Python
    • Control flow statements: 
    • If, Elif and Else
    • Definite and Indefinite loops: 
    • For and While loops


    • Built In Functions in Python
    • Paramerized functions
    • Writing user-defined functions in Python


    • Class
    • Object
    • Constructor


    • List
    • List comprehensions and Lambda
    • Parsing information with Python
    • Dictionaries
    • Tuples

    Python Programming Language

    • Statistical Hypothesis 
    • Testing Python Hypothesis
    • Testing Matplotlib Numpy

    Pandas Scipy Python 

    Lambdas Python 

    Regular Expressions

    Introduction to NumPy

    • The Basics of NumPy Arrays
    • Computation on NumPy Arrays: 
    • Universal Functions Aggregations: 
    • Min, Max 
    • Everything in Between Computation on Arrays: 
    • Broadcasting Comparisons, 
    • Masks, Boolean Logic Fancy 
    • Indexing Sorting 

    Arrays Structured Data: 

    NumPy’s Structured Arrays

    Advanced NumPy

    Introducing Pandas Objects

    • Data Indexing and Selection
    • Operating on Data in Pandas, 
    • Handling Missing Data
    • Hierarchical Indexing 

    Combining Datasets: 

    • Concat and Append Combining Datasets: 
    • Merge and Join
    • Aggregation and Grouping and Pivot Tables
    • Vectorized String 

    Operations Working with Time Series

    High-Performance Pandas: 

    • eval() and query()
    • Visualization with Matplotlib
    • Simple Line Plots
    • Simple Scatter Plots
    • Visualizing Errors, 
    • Density and Contour Plots
    • Histograms, 
    • Binnings, and Density
    • Customizing Plot Legends
    • Data Loading, Storage, and File Formats 
    • Data Cleaning and Preparation Data Wrangling Plotting and Visualization 
    • Data Aggregation and 
    • Group Operations 
    • Time Series 

    Advanced pandas 

    • Introduction to Modeling Libraries in Python Data Analysis Examples 
    • Customizing Colorbars 
    • Multiple Subplots Text 
    • and Annotation Customizing Ticks 

    Customizing Matplotlib: 

    • Configurations and Stylesheets 
    • Three-Dimensional Plotting in Matplotlib 
    • Geographic Data with Basemap Visualization with Seaborn 

    What Is Machine Learning? 

    • Introducing Scikit
    • Learn Hyper parameters and Model Validation 
    • Feature Engineering 
    • Naive Bayes Classification 
    • Linear Regression Support 
    • Vector Machines 
    • Decision Trees and Random Forests Principal Component Analysis 
    • Manifold Learning k-Means Clustering 
    • Gaussian Mixture Models 
    • Kernel Density Estimation 
    • A Face Detection Pipeline 


    • Variables and Placeholders 
    • TensorFlow – A Neural Network 
    • TensorFlow Regression 
    • TensorFlow Classification 
    • TF Classification 
    • Saving and Restoring Models 
    • Convolutional Neural Networks

    Introduction to Convolutional Neural Network Section 

    • Review of Neural Networks 
    • MNIST
    • MNIST Data Overview


    • Deep Nets with Tensorflow  
    • Abstractions API – Keras 



Real Life Case Studies

Real Life Case Studies

Projects modeled on select use cases with implementation of diverse technology concepts



All guided classes and courses are mandatorily followed by useful practical assignments

24x7 Expert Support

24x7 Expert Support

Every technical query is resolved on demand with readily available expert assistance

Instructor-led Sessions

Technical session conducted under the guidance of qualified and certified educationists

Course Info

Course Start Date 06/19/2020
Course End Date 08/31/2020
Estimated Duration 3/4 Weeks
Maximum Students 30
Levels Advanced

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