DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN BTM LAYOUT, BANGALORE

Live Virtual

Instructor Led Live Online

110,000
59,451

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Live Online Training
  • 25 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

66,000
34,951

  • Self Learning + Live Mentoring
  • IABAC® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 25 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Leaner assistance and support

Classroom

In - Person Classroom Training

110,000
64,451

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Classroom Sessions
  • 25 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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UPCOMING DATA SCIENCE ONLINE CLASSES IN BTM LAYOUT

UPCOMING DATA SCIENCE OFFLINE CLASSES IN BTM LAYOUT

BEST DATA SCIENCE CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES FOR DATA SCIENCE TRAINING

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE CERTIFICATION COURSE

MODULE 1: DATA SCIENCE ESSENTIALS 

 • Introduction to Data Science
 • Evolution of Data Science
 • Big Data Vs Data Science
 • Data Science Terminologies
 • Data Science vs AI/Machine Learning
 • Data Science vs Analytics

MODULE 2: DATA SCIENCE DEMO

 • Business Requirement: Use Case
 • Data Preparation
 • Machine learning Model building
 • Prediction with ML model
 • Delivering Business Value.

MODULE 3: ANALYTICS CLASSIFICATION 

 • Types of Analytics
 • Descriptive Analytics
 • Diagnostic Analytics
 • Predictive Analytics
 • Prescriptive Analytics
 • EDA and insight gathering demo in Tableau

MODULE 4: DATA SCIENCE AND RELATED FIELDS

 • Introduction to AI
 • Introduction to Computer Vision
 • Introduction to Natural Language Processing
 • Introduction to Reinforcement Learning
 • Introduction to GAN
 • Introduction to Generative Passive Models

MODULE 5: DATA SCIENCE ROLES & WORKFLOW

 • Data Science Project workflow
 • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
 • Data Science Project stages.

MODULE 6: MACHINE LEARNING INTRODUCTION

 • What Is ML? ML Vs AI
 • ML Workflow, Popular ML Algorithms
 • Clustering, Classification And Regression
 • Supervised Vs Unsupervised

MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS

 • Data Science in Finance and Banking
 • Data Science in Retail
 • Data Science in Health Care
 • Data Science in Logistics and Supply Chain
 • Data Science in Technology Industry
 • Data Science in Manufacturing
 • Data Science in Agriculture

MODULE 1: PYTHON BASICS 

 • Introduction of python
 • Installation of Python and IDE
 • Python Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

MODULE 2: PYTHON CONTROL STATEMENTS 

 • IF Conditional statement
 • IF-ELSE
 • NESTED IF
 • Python Loops basics
 • WHILE Statement
 • FOR statements
 • BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES 

 • Basic data structure in python
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS 

 • Functions basics
 • Function Parameter passing
 • Lambda functions
 • Map, reduce, filter functions

MODULE 1: OVERVIEW OF STATISTICS 

 • Introduction to Statistics
 • Descriptive And Inferential Statistics
 • Basic Terms Of Statistics
 • Types Of Data

MODULE 2: HARNESSING DATA 

 • Random Sampling
 • Sampling With Replacement And Without Replacement
 • Cochran's Minimum Sample Size
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • Sampling Error
 • Methods Of Collecting Data

MODULE 3: EXPLORATORY DATA ANALYSIS 

 • Exploratory Data Analysis Introduction
 • Measures Of Central Tendencies: Mean,Median And Mode
 • Measures Of Central Tendencies: Range, Variance And Standard Deviation
 • Data Distribution Plot: Histogram
 • Normal Distribution & Properties
 • Z Value / Standard Value
 • Empirical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4: HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

 

MODULE 1: MACHINE LEARNING INTRODUCTION 

 • What Is ML? ML Vs AI
 • Clustering, Classification And Regression
 • Supervised Vs Unsupervised

MODULE 2:  PYTHON NUMPY  PACKAGE 

 • Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3:  PYTHON PANDAS PACKAGE 

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

 • Visualization Packages (Matplotlib)
 • Components Of A Plot, Sub-Plots
 • Basic Plots: Line, Bar, Pie, Scatter

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSSION

 • Introduction to Linear Regression
 • How it works: Regression and Best Fit Line
 • Modeling and Evaluation in Python

MODULE 7: ML ALGO: LOGISTIC REGRESSION

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING

 • Understanding Clustering (Unsupervised)
 • K Means Algorithm
 • How it works : K Means theory
 • Modeling in Python

MODULE 9: ML ALGO: KNN

 • Introduction to KNN
 • How It Works: Nearest Neighbor Concept
 • Modeling and Evaluation in Python

MODULE 1: FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

 • Introduction to SVM
 • How It Works: SVM Concept, Kernel Trick
 • Modeling and Evaluation of SVM in Python

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

 • Building Blocks Of PCA
 • How it works: Finding Principal Components
 • Modeling PCA in Python

MODULE 4:  ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING 

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

 • Introduction to Naive Bayes
 • How it works: Bayes' Theorem
 • Naive Bayes For Text Classification
 • Modeling and Evaluation in Python

MODULE 7: GRADIENT BOOSTING, XGBOOST

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

 • What is Time Series?
 • Trend, Seasonality, cyclical and random
 • Stationarity of Time Series
 • Autoregressive Model (AR)
 • Moving Average Model (MA)
 • ARIMA Model
 • Autocorrelation and AIC
 • Time Series Analysis in Python 

MODULE 2: SENTIMENT ANALYSIS 

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON 

 • Regex Introduction
 • Regex codes
 • Text extraction with Python Regex

MODULE 4:  ML MODEL DEPLOYMENT WITH FLASK 

 • Introduction to Flask
 • URL and App routing
 • Flask application – ML Model deployment

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

 • MS Excel core Functions
 • Advanced Functions (VLOOKUP, INDIRECT..)
 • Linear Regression with EXCEL
 • Data Table
 • Goal Seek Analysis
 • Pivot Table
 • Solving Data Equation with EXCEL

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure, AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

 • Introduction to AZURE ML studio
 • Data Pipeline
 • ML modeling with Azure

MODULE 8:  INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION 

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

MODULE 2:  SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

MODULE 3: DATA TYPES AND CONSTRAINTS 

 • Numeric, Character, date time data type
 • Primary key, Foreign key, Not null
 • Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL) 

 • Create database
 • Delete database
 • Show and use databases
 • Create table, Rename table
 • Delete table, Delete table records
 • Create new table from existing data types
 • Insert into, Update records
 • Alter table

MODULE 5: SQL JOINS 

 • Inner Join, Outer Join
 • Left Join, Right Join
 • Self Join, Cross join
 • Windows function: Over, Partition, Rank

MODULE 6: SQL COMMANDS AND CLAUSES 

 • Select, Select distinct
 • Aliases, Where clause
 • Relational operators, Logical
 • Between, Order by, In
 • Like, Limit, null/not null, group by
 • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB 

 • Introduction of Document DB
 • Document DB vs SQL DB
 • Popular Document DBs
 • MongoDB basics
 • Data format and Key methods

MODULE 1: GIT  INTRODUCTION 

 • Purpose of Version Control
 • Popular Version control tools
 • Git Distribution Version Control
 • Terminologies
 • Git Workflow
 • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

 • Git Repo Introduction
 • Create New Repo with Init command
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

 • Code Commits
 • Pull, Fetch and Conflicts resolution
 • Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

 • Organize code with branches
 • Checkout branch
 • Merge branches
 • Editing Commits
 • Commit command Amend flag
 • Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET

 • Creating GitHub Account
 • Local and Remote Repo
 • Collaborating with other developers

MODULE 1: BIG DATA INTRODUCTION 

 • Big Data Overview
 • Five Vs of Big Data
 • What is Big Data and Hadoop
 • Introduction to Hadoop
 • Components of Hadoop Ecosystem
 • Big Data Analytics Introduction

MODULE 2 : HDFS AND MAP REDUCE 

 • HDFS – Big Data Storage
 • Distributed Processing with Map Reduce
 • Mapping and reducing stages concepts
 • Key Terms: Output Format, Partitioners,
 • Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION 

 • PySpark Introduction
 • Spark Configuration
 • Resilient distributed datasets (RDD)
 • Working with RDDs in PySpark
 • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

 • Introducing Spark SQL
 • Spark SQL vs Hadoop Hive

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2:  POWER-BI BASICS

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES 

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES 

• Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

OFFERED DATA SCIENCE COURSES IN BTM LAYOUT

DATA SCIENCE TRAINING COURSE REVIEWS

ABOUT DATA SCIENCE COURSE IN BTM LAYOUT

By 2025, brace yourself for a mind-boggling amount of data, as reports predict a daily surge of 463 exabytes! That's enough to fill a stack of DVDs reaching the moon and back over 32 times! This staggering amount of data highlights the urgent need for professionals with the right skills to analyze and make sense of this data. At DataMites, we offer comprehensive data science courses in BTM designed to equip students with the skills they need to succeed in this fast-growing field.

DataMites is an IABAC-accredited institute with a global reputation for providing high-quality data science education. Our expert faculty includes leading professionals and academics who bring their real-world experience to the classroom. We offer comprehensive training in all aspects of data science, from fundamentals to advanced techniques, ensuring that our students have the skills they need to succeed in this rapidly growing field. With our courses, you'll gain hands-on experience in working with real-world data sets, and learn how to use tools such as Python, R, SQL, and more.

At DataMites, we offer a range of courses to suit every student's needs. We provide data science offline training in BTM, where you can learn in a classroom setting and receive personalized attention from our instructors. For those who prefer to learn online, we offer online data science training in BTM, which provides flexibility and convenience while still offering the same quality of instruction. We also offer data science courses with internships in BTM, which provide valuable work experience and the opportunity to apply what you've learned in a real-world setting. And for those who want to get ahead in their careers, we offer data science courses with placement in BTM, which provide job opportunities with top companies in the field.

If you're looking to advance your career in data science, a data science certification from DataMites is the perfect solution. Our courses are designed to provide you with the knowledge and skills you need to excel in this exciting field. With an estimated growth rate of 25% annually, the demand for skilled data scientists has never been higher. Join DataMites, the leading global institute for data science courses in Bangalore, and unlock your potential today.

ABOUT DATAMITES DATA SCIENCE COURSE IN BTM LAYOUT

Data Science is a field that involves extracting insights and knowledge from data using scientific methods, processes, algorithms, and systems. It involves using a combination of programming skills, statistical knowledge, and subject matter expertise to identify patterns, make predictions, and gain insights from complex data sets.

In today's data-driven world, data is being generated at an unprecedented rate. Organizations are collecting vast amounts of data, and the ability to extract valuable insights from that data is crucial for making informed business decisions. Data Science plays a vital role in this process by providing the necessary tools and techniques to analyze, interpret, and visualize data in meaningful ways. This allows organizations to gain a competitive advantage by making data-driven decisions that can improve efficiency, reduce costs, and drive innovation.

Data Science Certification Courses in BTM are open to anyone who has an interest in learning about Data Science. This includes both newcomers to the field and professionals seeking to upskill. Individuals from various backgrounds such as engineering, marketing, software development, and IT can opt for part-time or external programs in data science. The minimum requirement for regular data science courses is a basic understanding of high school-level subjects.

The average data science course fee in BTM can vary depending on the course provider and the level of training. However, typically, it ranges from INR 40,000 to INR 1,00,000.

After completing Data Science Training in BTM, you can explore various job opportunities in the field of data analysis, data mining, business intelligence, and machine learning. Some of the popular job roles in the data science field include data scientist, data analyst, business analyst, data engineer, and machine learning engineer. You can expect to work in diverse industries such as healthcare, finance, e-commerce, marketing, and more. The demand for skilled data professionals is increasing every day, and you can expect a rewarding and challenging career in this field.

To excel in Data Science Courses in BTM, it's essential to have a strong foundation in mathematics and statistics. It is also important to have proficiency in programming languages such as Python and R, as well as familiarity with data visualization and analysis tools such as Tableau and Power BI. Additionally, critical thinking, problem-solving, and communication skills are highly valued in the field of data science.

Some potential challenges of learning Data Science include understanding complex mathematical concepts, writing code in programming languages like Python or R, working with large datasets, and keeping up with rapidly evolving technology and techniques. However, these challenges can be overcome with a structured learning approach, practice, and persistence. It is essential to choose the right training program that aligns with your learning objectives and skill level to ensure success.

Yes, it is possible for a fresher to enroll in a data science course in BTM and secure a job after completing the course. Many companies in the field of data science hire freshers with the right set of skills and aptitude for entry-level data analyst or data scientist roles.

Freshers who have completed a data science course in BTM can expect to find numerous job opportunities in the field of data science. Companies are always on the lookout for fresh talent that can help them make sense of the huge amounts of data they collect.

According to Glassdoor: The average Data Scientists Salary in Bangalore is INR 14,00,000 per annum.

FAQ'S OF DATA SCIENCE TRAINING IN BTM LAYOUT

DataMites has a strong reputation for delivering high-quality, industry-relevant Data Science courses. The institute provides hands-on training and practical experience to its students, which is essential in the field of Data Science. Additionally, DataMites offers comprehensive course content, industry-recognized certifications, and post-course support to help students succeed in their careers.

The duration of the DataMites data science course in BTM can range from 1 month to 8 months, depending on the course chosen. DataMites offers both weekday and weekend training sessions, so you can choose the session that suits your availability.

There are several institutes that offer Data Science training in BTM, but it is important to choose a reputable and experienced institute with certified trainers and practical hands-on training. DataMites is a globally recognized institute that offers comprehensive Data Science courses in BTM with experienced trainers, practical projects, and industry-recognized certifications.

The CDS course in BTM stands for Certified Data Scientist course, which is a comprehensive training program designed for individuals who wish to enter the field of Data Science and gain expertise in this domain

DataMites offers Data Science Offline Training in Bangalore at three locations - Kudlu Gate, BTM, and Marathahalli.

The Certified Data Scientist Training is designed for data science freshers who want to acquire the skills and knowledge to succeed in the field of data science.

DataMites offers a wide range of courses in Data Science, including Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Certified Data Scientist, Statistics for Data Science, and various industry-specific courses like Data Science Marketing, Operations, Retail, HR, Finance, etc.

The DataMites Data Science Training Fee BTM can range from Rs.28,000 to Rs.88,000, depending on the course and mode of training selected.

Yes, DataMites offers classroom training for their Data Science courses in BTM. Data Science Offline training provides a highly interactive learning experience, where students can interact with instructors and peers, ask questions and get immediate feedback. It also provides hands-on training and practical experience with real-time projects.

The Flexi-Pass is a unique offering from DataMites that allows you to attend Data Science training sessions for a period of 3 months. This pass can be used to revise any topic or clear any queries related to the course.

DataMites Placement Assistance Team provides job placement support to students who have completed their data science courses. They offer career guidance, resume building, and interview preparation to help students secure their desired job.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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