Certified Data Scientist Course by DataMites: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom +1 more	Mode of Delivery Video and Text Based

Course overview

DataMites’ Data Scientist certification course is a highly specialised programme covering the fundamentals of data science. The online Certified Data Scientist Course includes inclusive realms such as Statistics, Machine Learning, and Business Domain Knowledge to give you a 360-degree learning experience.

The Certified Data Scientist training course is available in both online and offline modes of learning. While it is mainly focused on Python, it also includes R to help professionals working in R. Throughout the course, you will receive expert guidance from elite professors and mentors via theoretical lessons and 10+ industry-related projects.

Moreover, after successful course completion, DataMites' dedicated Placement Assistance Team (PAT) will provide you with end-to-end job support. You'll work with expert counsellors for career enhancement. You will also receive unlimited access to Data Science Cloud Lab for practice.

Also, the Certified Data Scientist classes course is accredited by the International Association of Business Analytics Certification (IABAC). So, the certification holds global recognition.

Course and certificate fees

Fees information

₹ 59,451 ₹110,000

The Data Scientist certification course fee depends upon the mode of learning you choose.
You need to pay the entire course fee upfront.

Certified Data Science fee structure

Mode of learning	Total Fee	Discounted Fee
Live Virtual	Rs. 110,000	Rs. 59,451
Blended Learning	Rs. 66,000	Rs. 34,951
In-Person Classroom	Rs. 110,000	Rs. 64,451

certificate availability

Yes

certificate providing authority

IABAC

Who it is for

Data scientist Project manager

The Certified Data Scientist course is suitable for –

Beginners in the field who wish to get a solid understanding of data science for better career opportunities
Working professionals who want to switch their domain to data science
Those whose jobs mainly revolve around data analytics and Machine Learning
Project managers who wish to switch to management roles in data science projects

Eligibility criteria

There's no eligibility criteria for the Certified Data Scientist online training course.

Certification Qualifying Details

However, you need to clear the IABAC certification exam to qualify for the Certified Data Scientist Course certificate.

What you will learn

Data science knowledge Statistical skills Knowledge of data mining Knowledge of data visualization Machine learning

Once you have successfully completed the Certified Data Scientist programme by Data Mites, you will have –

A better understanding of the data science project workflow
Knowledge of statistical concepts
Understanding of core Machine Learning algorithms
Extensive knowledge of data mining practices, data forecasting, and data visualisation
Insights to create a business case for data science projects
Knowledge of delivering end-to-end projects to customers

The syllabus

DATA SCIENCE FOUNDATION

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

PYTHON FOUNDATION

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

STATISTICS ESSENTIALS

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

MACHINE LEARNING ASSOCIATE

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

MACHINE LEARNING EXPERT

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

ADVANCED DATA SCIENCE

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

DATABASE: SQL AND MONGODB

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

VERSION CONTROL WITH GIT

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

BIG DATA FOUNDATION

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

BI ANALYST

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

Admission details

Open the Certified Data Scientist course webpage.
Choose your preferred mode of learning and click on ‘Enquire Now.’
Now, create your account by providing the required contact information.
DataMites will contact you within 24 hours to discuss the next steps.

How it helps

The Data Mites’ Certified Data Scientist training course has been specially designed to cover the core concepts of data science. All the Certified Data Scientist Course modules comprise immensely valuable case studies and real-world projects. As a result, you can receive hands-on training and achieve a firm grip over the course material.

Besides, once you complete the course and pass the IABAC certification exam, you'll receive a globally recognised certificate. So, you can find lucrative job opportunities around the world.

FAQs

What if I miss a live training session of an online certified Data Scientist course?

All online sessions are recorded so you can view them later. In case of classroom sessions, you can speak to the coordinator to allow you to join the session in another batch.

Do I need to buy any software prior to starting with the programme?

No, most of the software used in the Certified Data Scientist Course is either free or open-source. So, you need not invest in any software prior to training.

What is the method of assessment for certification?

You will need to appear for a certification examination to qualify for the certificate.

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Popular Searches

Certified Data Scientist Course

Online

8 Months

₹ 59451 110000

Quick Facts

Course overview

The highlights

Program offerings

Course and certificate fees

Fees information

certificate availability

certificate providing authority

Who it is for

Eligibility criteria

What you will learn

The syllabus

DATA SCIENCE FOUNDATION

MODULE 1: DATA SCIENCE COURSE INTRODUCTION

MODULE 2: DATA SCIENCE ESSENTIALS

MODULE 3: DATA SCIENCE DEMO

MODULE 4: ANALYTICS CLASSIFICATION

MODULE 5: DATA SCIENCE AND RELATED FIELDS

MODULE 6: DATA SCIENCE ROLES & WORKFLOW

MODULE 7: MACHINE LEARNING INTRODUCTION

MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS

PYTHON FOUNDATION

MODULE 1: PYTHON BASICS

MODULE 2: PYTHON CONTROL STATEMENTS

MODULE 3: PYTHON DATA STRUCTURES

MODULE 4: PYTHON FUNCTIONS

MODULE 5: PYTHON NUMPY PACKAGE

MODULE 6: PYTHON PANDASPACKAGE

STATISTICS ESSENTIALS

MODULE 1: OVERVIEW OF STATISTICS

MODULE 2: HARNESSING DATA

MODULE 3: EXPLORATORY DATA ANALYSIS

MODULE 4: HYPOTHESIS TESTING

MODULE 5: CORRELATION AND REGRESSION

MACHINE LEARNING ASSOCIATE

MODULE 1: MACHINE LEARNING INTRODUCTION

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE

MODULE 3: VISUALIZATION WITH PYTHON

MODULE 4: ML ALGO: LINEAR REGRESSION

MODULE 5: ML ALGO: KNN

MODULE 6: ML ALGO: LOGISTIC REGRESSION

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA)

MODULE 8: ML ALGO: K MEANS CLUSTERING

MACHINE LEARNING EXPERT

MODULE 1: MACHINE LEARNING INTRODUCTION

MODULE 2: ML ALGO: LINEAR REGRESSSION

MODULE 3: ML ALGO: LOGISTIC REGRESSION

MODULE 4: ML ALGO: KNN

MODULE 5: ML ALGO: K MEANS CLUSTERING

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)

MODULE 7: ML ALGO: DECISION TREE

MODULE 8 : ML ALGO: NAÏVE BAYES

MODULE 9: GRADIENT BOOSTING, XGBOOST

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

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)

MODULE 12: ADVANCED ML CONCEPTS

ADVANCED DATA SCIENCE

MODULE 1: TIME SERIES FORECASTING - ARIMA

MODULE 2: FEATURE ENGINEERING

MODULE 3: SENTIMENT ANALYSIS

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

MODULE 8: AZURE FOR DATA SCIENCE

DATABASE: SQL AND MONGODB

MODULE 1: DATABASE INTRODUCTION

MODULE 2: SQL BASICS

MODULE 3: DATA TYPES AND CONSTRAINTS

MODULE 4: DATABASES AND TABLES (MySQL)

MODULE 5: SQL JOINS

MODULE 6: SQL COMMANDS AND CLAUSES

MODULE 7 : DOCUMENT DB/NO-SQL DB

VERSION CONTROL WITH GIT

MODULE 1: GIT INTRODUCTION