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DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE

Live Virtual

Instructor Led Live Online

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  • 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

1,240
789

  • 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

Corporate Training

Customize Your Training


  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING DATA SCIENCE COURSE ONLINE CLASSES

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 INSTITUTE FOR DATA SCIENCE COURSES

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSES

MODULE 1: DATA SCIENCE COURSE INTRODUCTION 

  • CDS Course Introduction
  • 3 Phase Learning
  • Learning Resources
  • Assessments & Certification Exams
  • DataMites Mobile App
  • Support Channels

MODULE 2: DATA SCIENCE ESSENTIALS 

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

MODULE 3: DATA SCIENCE DEMO 

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

MODULE 4: ANALYTICS CLASSIFICATION 

  • Types of Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

MODULE 5: 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 6: DATA SCIENCE ROLES & WORKFLOW

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

MODULE 7: MACHINE LEARNING INTRODUCTION

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

MODULE 8: 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 objects
  • Python basic data types
  • Number & Booleans, strings
  • Arithmetic Operators
  • Comparison Operators
  • Assignment Operators
  • Operator’s precedence and associativity

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
  • String object basics and inbuilt methods
  • List: Object, methods, comprehensions
  • Tuple: Object, methods, comprehensions
  • Sets: Object, methods, comprehensions
  • Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS 

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

MODULE 5: PYTHON NUMPY PACKAGE 

  • NumPy Introduction
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations

MODULE 6: PYTHON PANDASPACKAGE

  • Pandasfunctions
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

 

MODULE 1: OVERVIEW OF 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
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • 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
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4: HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5: CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

 

MODULE 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

  • NumPy & Pandas functions
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 3: VISUALIZATION WITH PYTHON 

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

MODULE 4: ML ALGO: LINEAR REGRESSION

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

MODULE 5: ML ALGO: KNN 

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

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

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

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA 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 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2: ML ALGO: LINEAR REGRESSSION 

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

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

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

MODULE 4: ML ALGO: KNN 

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

MODULE 5: ML ALGO: K MEANS CLUSTERING 

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

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

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

MODULE 7: ML ALGO: DECISION TREE 

  • Random Forest Ensemble technique
  • How it works: Bagging Theory
  • Modeling and Evaluation in Python

MODULE 8 : ML ALGO: NAÏVE BAYES 

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

MODULE 9: GRADIENT BOOSTING, XGBOOST 

  • Introduction to Boosting and XGBoost
  • How it works: weak learners' concept
  • Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

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

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

  • Introduction to ANN
  • How It Works: Back prop, Gradient Descent
  • Modeling and Evaluation of ANN in Python

MODULE 12: ADVANCED ML CONCEPTS 

  • Adv Metrics (Roc_Auc, R2, Precision, Recall)
  • K-Fold Cross-validation
  • Grid And Randomized Search CV In Sklearn
  • Imbalanced Data Set: Smote Technique
  • Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2: FEATURE ENGINEERING 

  • Introduction to Features Engineering
  • Transforming Predictors
  • Feature Selection methods
  • Backward elimination technique
  • Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS 

  • Introduction to Sentiment Analysis
  • Python packages: TextBlob, NLTK
  • Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

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

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

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

MODULE 7: AWS CLOUD FOR DATA SCIENCE

  • Introduction of cloud
  • Difference between GCC, Azure,AWS
  • AWS Service ( EC2 and S3 service)
  • AWS Service (AMI), AWS Service (RDS)
  • AWS Service (IAM), AWS (Athena service)
  • AWS (EMR), AWS, AWS (Redshift)
  • ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

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

MODULE 1: DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

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
  • Cross join
  • Self join

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
  • MongoDB data management

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
  • Copying existing repo
  • Git user and remote node
  • Git Status and rebase
  • Review Repo History
  • GitHub Cloud Remote Repo

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

MODULE 5: UNDOING CHANGES 

  • Editing Commits
  • Commit command Amend flag
  • Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET 

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

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
  • Hands-on Map Reduce task

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
  • Working with Spark SQL Query Language

MODULE 5 : MACHINE LEARNING WITH SPARK ML 

  • Introduction to MLlib Various ML algorithms supported by MLib
  • ML model with Spark ML
  • Linear regression
  • logistic regression
  • Random forest

MODULE 6: KAFKA and Spark 

  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION 

  • What Is Business Intelligence (BI)?
  • What Bi Is The Core Of Business Decisions?
  • BI Evolution
  • Business Intelligence Vs Business Analytics
  • Data Driven Decisions With Bi Tools
  • The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

  • The Tableau Interface
  • Tableau Workbook, Sheets And Dashboards
  • Filter Shelf, Rows And Columns
  • Dimensions And Measures
  • Distributing And Publishing

MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE 

  • Connecting To Data File , Database Servers
  • Managing Fields
  • Managing Extracts
  • Saving And Publishing Data Sources
  • Data Prep With Text And Excel Files
  • Join Types With Union
  • Cross-Database Joins
  • Data Blending
  • Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS 

  • Getting Started With Visual Analytics
  • Drill Down And Hierarchies
  • Sorting & Grouping
  • Creating And Working Sets
  • Using The Filter Shelf
  • Interactive Filters
  • Parameters
  • The Formatting Pane
  • Trend Lines & Reference Lines
  • Forecasting
  • Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES 

  • Dashboards And Stories Introduction
  • Building A Dashboard
  • Dashboard Objects
  • Dashboard Formatting
  • Dashboard Interactivity Using Actions
  • Story Points
  • Animation With Pages

MODULE 6: BI WITH POWER-BI 

  • Power BI basics
  • Basics Visualizations
  • Business Insights with Power BI

OFFERED DATA SCIENCE COURSES

DATA SCIENCE CAREER SUCCESS STORIES

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING COURSE

Data Science is marking its graph on a high note by expanding its width in creating great career opportunities currently. It is one of the most happening fields in business today. DataMites is offering the Data Science Training program on wide range of aspects. The certifications from DataMites are IABAC (International Association of Business and Analytics Certification) accredited which is a global certification. The course is designed (for both beginners and professionals) to enhance their skill basket and achieve their career goals. The course includes different key branches that would hold data science tight together, such as, Certified Data Science course, Statistics for Data Science course, Python for Data Science course, Data Scientist with R course and Diploma in Data Science. The mentioned courses’ details can be looked into on their respective tabs.

DataMites offers training on weekends as well as weekdays which has different modes of training which could be chosen by the trainees to opt for,

  1. Classroom Training
  2. Online Live Virtual Training
  3. e-Learning

According to the US Bureau of Labor Statistics, there will be a projected employment of 11.5 million in the field of data science and analytics by 2026, which is just five years from now. This demonstrates the high demand for skilled professionals in this field. At DataMites, we offer an exceptional Data Science Course that will equip you with the knowledge and skills needed to thrive in this exciting field.

Are you aware?
As per Glassdoor.com, the Average Data Scientist Salary in USA is $1,04,043 per annum.  Data Scientist Training can help you break through in the data science field!

DataMites is committed to establishing itself as a leading training institute in India, renowned for providing top-notch education in Data Science and related domains. Having successfully surpassed the milestone of 50,000 learners, we take pride in our achievements. Accredited by the International Association of Business Analytics Certification (IABAC), DataMites is globally recognized for its high standards. Our courses are conducted by proficient subject matter experts who possess deep knowledge and expertise in their respective domains. Our primary goal is to shape and mold experts who can fearlessly tackle the challenges of the competitive analytics landscape. Are you ready to embark on an exciting journey in the field of Data Science?

We provide 8 monthly Data Science Courses with both Data Science Online Training and Data Science Classroom Training that would be imparted within a three-phase learning method. 

Phase 1: Begin your journey by preparing for the upcoming course. We provide candidates with high-quality self-study videos and books that ensure a comprehensive understanding of the curriculum.

Phase 2: Immerse yourself in the primary stage of live intensive training, complemented by hands-on capstone projects. Upon completion of the training, you will be awarded the prestigious IABAC Data Science Certification, a globally recognized credential.

Phase 3: Take your skills to the next level through engaging projects, valuable internships, and our job-ready program. This phase is designed to enhance your practical experience and prepare you for successful employment in the field of Data Science.

We offer a diverse range of courses in Data Science to cater to different skill levels and interests:

Certified Data Scientist: This course provides a comprehensive understanding of Data Science and is suitable for individuals looking to dive deeper into the field. The training duration is 8 months, and it has been successfully completed by over 25,000 learners with the guidance of our experienced faculty.

Data Science Foundation: The course is tailored to provide the perfect starting point. It delves into the fundamentals of Data Science while also shedding light on the distinctions between Data Science, Business Analytics, and Big Data. Gain a comprehensive understanding of different classifications of Business Analytics, explore the workflow of Data Science projects, familiarize yourself with various roles in the field, and uncover the practical applications of Data Science across industries. 

Data Science Specialization Courses:

Data Science for Managers: This program is specifically designed for managers who want to gain insights into the tools and techniques used in Data Science. It covers topics such as Data Science Foundation, Statistics for Data Science, Building and Optimizing Models, Predictive Analysis and Regression, Decision Making, Visual Analytics, and Tableau.

Data Science for Finance: This course explores the application of Data Science in finance, including career opportunities in areas such as cybersecurity, machine learning, AI, and blockchain development.

Data Science for Operations: Learn how to collect and analyze data to make data-driven decisions in operational management.

Data Science for Marketing: Uncover the power of Data Science to drive targeted customer profiling, optimize search engine strategies, enhance customer engagement, and create impactful real-time marketing campaigns. 

Data Science for HR: Gain insights into the application of Data Science in HR, enabling better employee management and cost-effective decision-making.

Data Science with R: A 6-month training program that covers comprehensive training on R programming, Python, statistics, machine learning algorithms, business aspects, and Tableau. Master data manipulation, data visualization, regression, and data mining using RStudio.

Statistics for Data Science: Understand the role of statistics in processing complex problems and extracting meaningful insights from data.

Python for Data Science: Learn Python programming language and its application in data science projects, including data manipulation, visualization, and exploratory data analysis.

DataMites offers a range of flexible learning options to accommodate different preferences, including Data Science Classroom Courses, Data Science Online Courses, and access to exceptional recorded sessions. According to a LinkedIn report, data science continues to dominate emerging job rankings, experiencing an impressive growth of 37% over the past three years!

Why choose DataMites for your Data Science Training:

Expert Faculty with Ashok Veda as Lead Mentor: Ashok Veda is a highly experienced senior management professional, entrepreneur, and passionate advocate of artificial intelligence. With a diverse background spanning over two decades, he possesses extensive expertise in business management, consulting, project leadership, and successfully translating numerous concepts in IoT, AI, and ML into practical solutions. The entire faculty at DataMites operates under the exceptional guidance of Ashok Veda.

The faculty members at DataMites consist of seasoned experts with substantial industry experience in data science. They bring with them years of knowledge and actively contribute to research and development endeavors at DataMites.

Comprehensive Course Curriculum: Our course curriculum is carefully designed to cover all the essential topics in data science, ensuring you gain in-depth knowledge and skills.

Global Certification: We provide industry-recognized certifications from IABAC, Jain University and NASSCOM FutureSkills upon successful completion of the courses, giving you a competitive edge in the job market.

Flexible Learning Options: DataMites offers flexible learning options, including live online classes, self-paced learning, and classroom training, allowing you to choose the mode that suits your schedule and learning preferences.

Real-World Projects: Our training programs include hands-on projects using real-world data, providing practical experience and enhancing your problem-solving skills.

Internship Opportunities: DataMites offers data science internship opportunities, allowing you to apply your knowledge in real-world scenarios and gain practical industry experience.

Placement Assistance: We have a dedicated placement assistance team that provides guidance and support in finding job opportunities. We also provide job references to help you kick-start your career in data science.

Learning Materials: We provide hardcopy learning materials and books to supplement your learning and serve as a valuable reference throughout your data science journey.

Exclusive Learning Community: DataMites has an exclusive learning community where you can connect with fellow learners, industry experts, and mentors to exchange knowledge, collaborate, and grow together.

Affordable Pricing and Scholarships: DataMites offers cost-effective training programs without compromising on quality. We also provide scholarships to eligible candidates, making data science education more accessible.

The data revolution is transforming industries worldwide, with data serving as the driving force behind progress. The demand for Data Scientists is soaring, and companies are expected to seek even more professionals in the near future. The demand for data scientists will continue to rise, surpassing the available supply. Data science job opportunities are increasing globally, including in India, where the data landscape is expanding rapidly. It is crucial for IT professionals to update their data science skills to keep up with the growing demand.

By enrolling in Data Science Training Courses, you can pave the way to lucrative career opportunities in the top-tier IT systems. Becoming a Data Scientist has never been more accessible and attainable. With proper training and upskilling, you can position yourself for a rewarding career in the field of Data Science.

ABOUT DATAMITES DATA SCIENCE COURSES

DataMites™ Certified Data Scientist Training is designed to provide a right blend of all four facets of Data Science

  • This four facets form four pillars for data science field. They are 1. Programing 2. Statistics 3. Machine Learning 4. Business Knowledge.
  • The course is mainly focussed on Python for core data science programing; it also includes R as necessary to enhance professionals working in R.
  • Statistics are covered as required for a Data Scientist, you may find detailed syllabus in syllabus tab.
  • Machine Learning is the main tool kit for Data Science in predicting classification or regression.
  • This course covers all popular ML algorithms as detailed in syllabus tab.
  • This course allows candidates to obtain an in-depth knowledge by laying a strong foundation and covering all the latest data science topics.
  • The increasing demand curve for data science professionals to manage the large set of data in various organizations providing millions of job opportunities in global markets.
  • The knowledge gained through this course along with IABAC™ certificate surely helps you to become data science professional.

This course comes as a perfect package of required Data Science skills including programing, statistics and Machine Learning. If you aspire to be Data Science professional, this course can immensely help you to reach your goal.

After successful completion of this “Certified Data Scientist” course, you should have

  • Gained a better knowledge on entire Data Science project work flow.
  • Understand key concepts of statistics
  • Gained hands on knowledge of popular Machine learning algorithms
  • In depth knowledge on Data Mining, Data forecasting, and Data Visualization.
  • Able to create business case for Data Science project
  • Deliver end to end data science project to the customer

Data science is the hottest field in the market as of today. Be it a small company or an MNC, they need a Data scientist to manage their large pool of data.

  • High demand for data scientists with only a few qualified people who are eligible to get hired.
  • High salaries, nearly twice of an average software engineer as per Glassdoor report
  • This course is not only designed to enable new career opportunities for you but also allows you to apply the new age skills in your current work and become valuable to in your current role.
  • Be assured that you are entering the future of data science much earlier to grab those wonderful opportunities arising from this biggest need of the business world.

This course “Certified Data scientist” is not restricted to any specific domain.

  • Fresh Graduates or students from any discipline can choose this course to obtain better job opportunities in this most demanding data science field
  • Working professionals looking to change their domain to data science field.
  • Highly recommended for those who are aspiring jobs that mainly revolves around data analytics and machine learning
  • Project managers aspiring to switch to manage Data Science projects

DataMites™ is the global institute for Data Science accredited by International Association of Business Analytics Certifications (IABAC). DataMites provides flexible learning options from Classroom training, Live Online to high quality recorded sessions

The 6 Key reasons to choose Data Mites™

IABAC™ Accredited

  • Globally reputed certification
  • Syllabus Aligned with IABAC global market standards

Elite Faculty & Mentors

  • Best in industry faculty from IIMs
  • Course structured by Professors in Data Science from top universities
  • Ensures high quality learning experience

Learning Approach

  • Learning through case study approach
  • Theory → Hands On → Case Study → Project → Model Deployment

10+ Industry Projects

  • 10+ Industry related projects
  • Enabling candidates to gain real time skills, also boosting confidence for real challenges

PAT (Placement Assistance Team)

  • Dedicated PAT (Placement assistanceTeam)
  • Resume assist service
  • Mapping candidates to verified jobs by PAT team
  • Supporting in Interview preparation

24x7 Cloud Lab for ONE year

  • High capacity data science cloud lab
  • All Machine Learning python and R scripts on cloud lab for quick reference
  • Enable participants to practice Data Science even with their mobile phones through cloud lab

Data, in its vast and complex form, holds the potential to be transformed into valuable information. In the field of data science, the focus lies on extracting meaningful insights from large datasets, comprising both structured and unstructured data. By employing advanced techniques, data scientists are able to uncover hidden patterns and uncover actionable insights that can drive decision-making and create tangible value. Through the process of mining and analyzing data, data science brings forth the power to unlock the true potential of information.

  • Data is nothing without science
  • Better customer experience
  • Increase job opportunities
  • Rising salary for data science professionals
  • A multitude of job titles awaits your exploration and selection
  • You will have a significant role in shaping decision-making processes within the company.

Individuals of all backgrounds, whether newcomers or seasoned professionals, who possess an interest in learning Data Science, can readily pursue this field. Engineers, marketing professionals, software and IT professionals, among others, have the opportunity to enroll in part-time or external data science programs. Regular data science courses typically require a minimum prerequisite of basic high school level subjects.

The cost of data science courses may vary depending on the level of training you seek. When considering the fee structure for classroom training in data science, it typically ranges from 1000 USD to 3000 USD, depending on the training provider you choose.

  • Data scientist
  • Machine learning engineer
  • Machine-learning scientist
  • Application architect
  • Data architect
  • Data engineer
  • Statistician
  • Data Analyst
  • Business intelligence analyst
  • Marketing analyst

The basic skills required to learn Data Science include programming knowledge (such as Python or R), statistical analysis, data manipulation and visualization, machine learning algorithms, and critical thinking/problem-solving abilities.

Proficiency in programming languages such as Python, R, Excel, C++, Java, and SQL is highly preferred in the field of Data Science. However, it is possible to start with the fundamentals and continually enhance your skills in these areas.

Data Science can be challenging due to its complex concepts and techniques. It requires a solid understanding of mathematics, statistics, programming, and domain knowledge. However, with dedication, proper learning resources, and practice, it is possible to overcome the challenges and become proficient in Data Science.

Python, R, SQL, Tableau, Apache Spark, TensorFlow, and PyTorch are commonly used tools in Data Science.

Data science has now surpassed almost every business on the earth. There isn't a single industry on the planet that doesn't rely on data these days. As a result, data science has turned into a source of energy for companies. Data Science is applicable in industries including travel, healthcare, sales, credit and insurance, marketing, social media, automation and substantially more!

By 2025, global data is projected to reach 175 zettabytes, as reported by IDC. Data Science plays a crucial role in helping companies effectively analyze and utilize large volumes of data from various sources, enabling them to make informed and data-driven decisions. Its applications span across diverse industries including marketing, healthcare, finance, banking, and policy work. The importance of data science is undeniable, given its widespread use and impact.

Yes, it is definitely possible to switch from a mechanical background to a career in data science. While a background in mechanical engineering may not directly align with data science, it can provide you with a strong foundation in mathematics, problem-solving skills, and analytical thinking, which are valuable in the field of data science.

Freshers are indeed hired for Data Scientist positions in companies. In India, many entry-level analytics jobs do not require any specialization or post-graduation. The primary qualification sought by these companies is an engineering degree, irrespective of the stream. They focus on assessing your aptitude, communication skills, and critical reasoning abilities rather than specific academic backgrounds.

Yes, it is possible to switch from a non-coding background to a data science background. While having prior coding experience can be advantageous, it is not a strict requirement for entering the field of data science. Many individuals with non-coding backgrounds have successfully transitioned into data science roles.

  • As per Glassdoor.com, the national average Data Scientist salary in the United States is USD $1,04,043 per year.
  • The national average Data Scientist salary in UK is £49,710 EUR per annum. (Payscale)
  • The national average Data Scientist salary in India is INR₹12,00,000 per year. (Glassdoor)

The field of Data Science is extensive, and its applications are limitless. Companies worldwide are actively seeking data science professionals who can contribute to their organizations. Obtaining data science certifications can be highly beneficial for your career in today's technology-driven world. It enhances your skillset and increases your prospects in the job market.

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FAQ’S OF DATA SCIENCE TRAINING COURSE

Total course fee should be paid before 50% of the course completion. We also have EMI option tied up with bank. Check with coordinators.

No, most of the software is free and open source. The guidelines to setup software are a part of course.

Certified Data Scientist is delivered in both Classroom and Online mode. Classroom is provided in selected cities in India such as Bangalore, Hyderabad.

Yes. The IABAC Exam fee is included in the course fee. No extra fee is charged.

All the online sessions are recorded and shared so you can revise the missed session. For Classroom, speak to the coordinator to join the session in another batch.

We have a dedicated PAT (Placement Assistance team) to provide 100% support in finding your dream job.

Yes, statistics is an essential component of data science and plays a crucial role in achieving accurate results and making informed decisions. Statistics allows data scientists to analyze and interpret data, apply various statistical techniques like classification, regression, hypothesis testing, and time series analysis, and build robust data models. It provides the foundation for understanding data patterns, relationships, and uncertainties, ultimately improving the quality of insights and driving effective decision-making in data science.

Python is widely regarded as a fundamental tool in the field of data science. In fact, a significant majority of data scientists, around 66% according to a 2018 survey, reported using Python on a daily basis. Python's popularity stems from its versatility, extensive libraries and frameworks, and ease of use for data manipulation, analysis, and machine learning tasks. While there are other programming languages used in data science, having proficiency in Python or any programming language is considered essential to effectively carry out data science work.

DataMites renders Data Science Training in:

  • Certified Data Scientist Course
  • Data Science Foundation
  • Data Science for Managers
  • Python for Data Science
  • Statistics for Data Science
  • Diploma in Data Science
  • Data Science Marketing
  • Data Science Operations
  • Data Science Retail
  • Data Science for HR
  • Data Science Finance

At Datamites, the following beginner-level data science courses are available:

Certified Data Scientist (CDS): This course is designed for individuals who want to start their journey in data science. It covers the fundamentals of data science, including Python programming, data analysis, machine learning algorithms, data visualization, and model deployment. The course also includes hands-on projects and case studies to provide practical experience.

Data Science Foundation: This course provides a solid foundation in data science concepts and techniques. It covers topics such as Python programming, data manipulation, exploratory data analysis, statistical analysis, machine learning algorithms, and data visualization. The course aims to equip students with the essential skills needed to kickstart a career in data science.

Diploma in Data Science: This comprehensive program is designed for beginners and covers a wide range of data science topics. It includes modules on Python programming, data preprocessing, exploratory data analysis, feature engineering, machine learning algorithms, deep learning, natural language processing, and big data analytics. The course incorporates hands-on projects and industry-relevant case studies to enhance practical skills.

offers a course specifically designed for C-level executives and business owners called "Data Science for Managers." This course focuses on providing a comprehensive understanding of data science concepts, strategies, and applications from a managerial perspective. The course aims to enhance the data science competency of managers and business leaders, enabling them to make informed decisions based on data-driven insights. 

The Data Science Course has a duration of 8 months, with a total of 700 hours of training. Training sessions are available on both weekdays and weekends, allowing you to choose the option that best suits your availability and schedule.

While a postgraduate degree is not necessarily a requirement, having prior knowledge in areas such as Mathematics, Statistics, Economics, or Computer Science can greatly benefit your understanding and proficiency in Data Science. These foundational subjects provide a solid basis for grasping the concepts and techniques used in the field. However, even if you don't have a PG degree or specific background, with dedication and the right resources, you can still learn and excel in Data Science.

Data Science is one of the best spheres where you can begin your career in. Freshers can enroll for Certified Data Scientist Course and Data Science Foundation Course or Diploma in Data Science.

Professionals who wish to enhance their professional capabilities can enroll for:

  • Statistics for Data Science
  • Data Science with R Programming Course
  • Python for Data Science
  • Data Science Associate
  • Certified Data Scientist Operations
  • Data Science with R Programming Course

DataMites offers specialized courses for senior managers and business owners, including courses on;

  • Data Science for Managers
  • Certified Data Scientist Marketing Course
  • Certified Data Scientist HR Course
  • Certified Data Scientist Finance Course

A certified data scientist is an individual who has obtained comprehensive knowledge in the field of data science. The Certified Data Scientist Training is specifically tailored for those who aspire to enter the Data Science domain with a strong foundation and the necessary skills to excel in this field. The course provides thorough guidance and equips participants with the best practices and expertise required to succeed in the data science industry. By completing the course and earning the certification, individuals can demonstrate their proficiency and readiness to tackle real-world data science challenges.

The CDS (Certified Data Scientist) Course is specifically designed for aspiring data science professionals who are new to the field and aim to make a significant impact in the world of Data Science. This course is structured to provide comprehensive knowledge and skills required to excel in the field of data science.

  • DataMites™ is a globally recognized institute for data science, accredited by the prestigious International Association of Business Analytics Certification (IABAC).
  • With an impressive enrollment of over 25,000 students, we are committed to providing top-notch education in the field of data science.
  • Our comprehensive learning approach consists of three phases. In Phase 1, candidates will have access to self-study videos and books, allowing them to gain a solid understanding of the syllabus. Phase 2 involves intensive live online training, providing hands-on experience and practical knowledge. Finally, in Phase 3, we offer exciting real-world projects and valuable case studies to further enhance your skills.
  • Upon successful completion of the training, you will be awarded the globally recognized IABAC certification, validating your expertise in the field of data science.
  • To provide you with practical industry exposure, we offer internship opportunities with Rubixe, a renowned global technology company specializing in artificial intelligence.
  • At DataMites™, we are dedicated to shaping your data science journey and empowering you with the knowledge and skills to excel in this dynamic field.

The fees for the Data Science Course will vary depending on the specific program and level of training you choose. 

  • The USA -  USD 828 to USD 2,060
  • India - INR  43,995 to INR 110,000
  • Europe - 660 Euro to 1,650 Euro

DataMites provides classroom training for Data Science courses primarily in Bangalore. However, we understand the demand for training in other locations as well. We are open to hosting classroom sessions in other locations based on the demand and availability of interested applicants in those areas. Please contact us with your location preference, and we will do our best to accommodate your needs and schedule a training session in your desired location.

At DataMites, we prioritize the selection of trainers who possess the required qualifications and extensive expertise in the field of Data Science. Our trainers are carefully selected based on their industry experience, certification, and comprehensive understanding of the subject matter. We strive to ensure that our trainers have accumulated decades of practical experience and are well-versed in the latest trends and techniques in the field of Data Science. Rest assured that our trainers are highly knowledgeable and capable of delivering high-quality training to our students.

At DataMites, we understand that everyone has different learning preferences. That's why we offer flexible options including live online, self-paced, and classroom training. Choose the method that suits you best and embark on your data science journey with us.

With the DataMites Flexi-Pass for Data Science training, you'll have a 3-month window to attend our sessions. Take advantage of this opportunity to clarify doubts and review topics according to your preference. We are dedicated to supporting your learning journey every step of the way.

Upon successful completion of the Data Science training, you will be awarded an internationally recognized IABAC® certification, validating your proficiency in the field and enhancing your global employability.

Upon successful completion of the course, you will receive a Course Completion Certificate from us, acknowledging your successful accomplishment and demonstrating your dedication and competence in the field of Data Science.

Yes, to ensure authenticity and accuracy, we require participants to provide a valid photo ID proof such as a National ID card or Driving License for issuing the participation certificate and booking the certification exam, as per the requirements.

Simply reach out to your instructors and coordinate a class time that suits your schedule. For Data Science Training Online, all sessions will be recorded and made available for easy access, allowing you to catch up on missed content at your own convenience.

Certainly! We offer a complimentary demo class to provide you with a glimpse of the training process and give you an overview of what to expect. This will help you understand the training methodology and content before making a decision to enroll.

Absolutely! At DataMites, we have a dedicated Placement Assistance Team (PAT) that works tirelessly to provide placement support to our students. Once you successfully complete the course, our team will assist you in finding suitable job opportunities and guide you through the placement process. We strive to help you kickstart your career in the field of Data Science.

The DataMites Placement Assistance Team (PAT) is dedicated to supporting applicants in every step of their Data Science career journey. PAT offers a range of services including:

  • Job placement assistance to help connect applicants with suitable job opportunities.
  • Resume creation and optimization to highlight their skills and experience effectively.
  • Conducting mock interviews with industry professionals to provide practice and valuable feedback.
  • Guiding applicants on interview preparation and providing insights into common interview questions.

With the help of our PAT, applicants can enhance their chances of securing a successful career in the field of Data Science.

The DataMites Placement Assistance Team (PAT) offers career coaching sessions to applicants, aimed at helping them identify their roles and purpose in the corporate sector. Industry experts provide guidance on the various opportunities available in the Data Science career, giving applicants a comprehensive understanding of their options. They also learn about the potential challenges they may face as newcomers and strategies to overcome them. These sessions empower applicants to make informed decisions and navigate their career paths effectively in the Data Science field.

We encourage you to make the most of your training experience. If you need a better understanding of any topic, you can request a help session or seek additional clarification from your instructors. We are here to support your learning journey and ensure that you have a comprehensive understanding of the course material.We encourage you to make the most of your training experience. If you need a better understanding of any topic, you can request a help session or seek additional clarification from your instructors. We are here to support your learning journey and ensure that you have a comprehensive understanding of the course material.

We offer multiple payment methods to make it convenient for you. You can choose to make your payment using any of the following methods:

  • Cash
  • Net Banking
  • Check
  • Debit Card
  • Credit Card
  • PayPal
  • Visa
  • Master card
  • American Express

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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