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
  

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.

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.