Become a Masters in Data Science
and Machine Learning

Data Science Training: Statistics, R, Python, Advanced Statistics in Python, Machine & Deep Learning, NLP, Tableau, Data Visualization, Data Structure Analysis (Numpy & Pandas), Data Preprocessing.

Batches
110+
Batches
Professionals
1650+
Professionals Trained
duration
Duration
3 Months
4.9/5
4.8/5
Rating Given By Students
Data Science

Most Trusted Data Science Training Institute in Bangalore

Data has become the next currency and Data science professionals are in huge demand for their skills and expertise. School of Data Science has 4 centres of learning in Bangalore that are conveniently located at prominent places of the city. Call us for your free information counselling and demo session. We offer the best data science course in Bangalore that makes you industry ready in three months.

  • Flexible pricing options
  • Designed for Working
    Professionals/Students
  • Real-life Case Studies
  • Assignments
  • Certification

COURSE OPTIONS

Best Data Science Training Institute in Bangalore. We are responsible for the right education from day one of joining.Learn
Statistics, R, Python, Advanced Statistics in Python, Machine & Deep Learning, NLP, Tableau, Data Visualization, Data Structure Analysis,
Data Preprocessing Courses In Bangalore with Affordable Course Fees.

Live Virtual

Instructor Led Live Online


  • Flexible pricing options
  • Designed for Working
    Professionals/Students
  • Real-life Case Studies
  • Assignments
  • Certification


classes img

Classroom

In - Person Classroom Training


  • Flexible pricing options
  • Designed for Working
    Professionals/Students
  • Real-life Case Studies
  • Assignments
  • Certification


What Our Students Say

Data Science Course Curriculum

The only Data Science training program
where you get in-depth knowledge of all the modules of Data Science

  • Variables and Datatypes
  • If, else-if and else statements
  • Comparison operators
  • Loops - For, While & Repeat
  • Exploratory data analysis
  • Exercises and assignment challenge
    1. Handling
      • Data types
      • Missing Values
      • Outliers
    1. Statistical Data visualization using GGPlot
    2. Working statistical graphs
      • BOX Plot
      • Regression
      • Scatter
      • Histogram (Distribution)
      • Density chart
    3. Exercises and assignment challenge
    1. Algorithms
      • Linear regression Analysis
      • Logistic Regression
      • Decision Tree & Random Forest
      • Kmeans Cluster
      • KNN
  • Variables and Datatypes
  • If, else-if and else statements
  • Comparison operators
  • Loops - For, While
  • Introduction to data analysis
  • Why Data analysis?
  • Exploratory Data Analysis
  • Creating and applying functions
  • Numpy Indexing and selection
  • Numpy Operations
  • Exercise and assignment challenge
  • Exploratory data analysis
  • Exercises and assignment challenge
    1. Handling
      • Data types
      • Missing Values
      • Outliers
    1. Statistical Data visualization using Seaborn
    2. Working statistical graphs
      • BOX Plot
      • Regression
      • Scatter
      • Histogram (Distribution)
  • Introduction Lesson
  • Sample or Population Data?
  • The Fundamentals of Descriptive Statistics Lesson
  • Measures of Central Tendency, Asymmetry, and Variability Lesson
  • Practical Example: Descriptive Statistics
  • Distributions Lesson
  • Estimators and Estimates Lesson
  • Confidence Intervals: Advanced Topics Lesson
  • Practical Example: Inferential Statistics Lesson
  • Hypothesis Testing: Introduction Lesson
  • Hypothesis Testing: Let’s Start Testing! Lesson 1
  • Practical Example: Hypothesis Testing Lesson 1
  • The Fundamentals of Regression Analysis Lesson
  • Subtleties of Regression Analysis Lesson
  • Assumptions for Linear Regression Analysis
  • Getting Started with Tableau Lesson
  • Core Tableau in Topics
  • Creating Charts in Tableau Lesson
  • Filters in Tableau
  • Applying Analytics to the worksheet
  • Dashboard in Tableau Lesson
  • Modifications to Data Connections
  • Introduction to data analysis
  • Exploratory Data Analysis
  • Why Data analysis?
  • Creating and applying functions
  • Matrices Indexing and selection
  • Matrices Operations
  • Exercise and assignment challenge
  • FIND US

    X