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Data Science Online Course

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Data Science Online Course

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ThinkNEXT Technologies Private Limited has introduced Data Science Online Course for those who can easily avail the Digital Marketing Training Online anywhere in India. The motive of digital marketing classes online is to educate students, working professionals, freelancers, entrepreneurs, business owners etc. in the field of digital marketing, so that they could upgrade their skills from home. ThinkNEXT’s best Data Science Online Courses provides its students with -hands on exposure to 40+ must-know tools to implement Digital Marketing strategies from basic to advanced level.

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Doubt clearing

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Beginner friendly

No prior knowledge required

6 weeks access

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Downloadable content

With lifetime access

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Why Online Training from ThinkNEXT

8 Times National Award Winning Institute
Online Training from Google Partner, Facebook Blueprint Certified, Hubspot Certified and Bing Certified Institute
Industry Expert Trainers: We have highly experienced and certified Professionals/Trainers equipped with in-depth knowledge
Affordable Fees: Join our Online Training Courses with the lowest fees
Professional Online Course in India at ThinkNEXT with International Certifications from Microsoft and Hewlett Packard
Step-by-step live demonstrations for each software/technology
Enjoy the undivided attention of the mentor to learn faster

Personalized study and training plans are provided as you progress through the course
Have the exact same learning experience as being in a traditional classroom training
Same projects and evaluation techniques will be followed like classroom courses
Learn new technologies from the comfort of your home
Get all the tips and tricks to crack the interview
100% Placement Support: We have an expert team to line up your placement calls until you get the job
Grooming Sessions: Spoken English, Personality Development

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What will be the training syllabus?

Module1: Introduction

What Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project
Why Python for data science?

Module2:Core Python

Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
Python Syntax
Variables & Data Types
Operators
Conditional Statements
Working With Numbers & Strings
Collections API
LISTS
TUPLES .
DICTIONARY
Date and Time
Function & Modules
File handling
Exception Handling
OOPS Concepts in python
Regular Expression

Module 3: Python Libraries for Data Science

Numpy
Scify
pandas
scikitlearn
statmodels
nltk

Module 4: Python Modules for Access, Import/Export Data

Importing Data from various sources (Csv, txt, excel, access etc.)
Database Input (Connecting to database)
Viewing Data objects - subsetting, methods
Exporting Data to various formats
Important python modules: Pandas, beautiful soup

Module 5: Data Manipulation, Cleansing and Munging

Cleansing Data with Python
Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc.)
Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc.)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc.)

Module 6: Data Analysis and Visualization

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc.)
Data visualization with tableau.

Module 7: Statistics

Basic Statistics - Measures of Central Tendencies and Variance
Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
Inferential Statistics -Sampling - Concept of Hypothesis Testing
Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
Important modules for statistical methods: Numpy, Scipy, Pandas

Module 8: Predictive Modeling

Concept of model in analytics and how it is used?
Common terminology used in analytics & modeling process
Popular modeling algorithms
Types of Business problems - Mapping of Techniques
Different Phases of Predictive Modeling

Module 9: Data Exploration for Modeling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns

Module 10: Data Preparation

Need of Data preparation
Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
Variable Reduction Techniques - Factor & PCA Analysis

Module 11: Solving Segmentation Problems

Introduction to Segmentation
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioral Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling - Identify cluster characteristics
Interpretation of results - Implementation on new data

Module 12: Linear Regression

Introduction - Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results - Business Validation - Implementation on new data

Module 13: Logistic Regression

Introduction - Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model (Binary Logistic Model)
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logist ic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
Interpretation of Results - Business Validation - Implementation on new data

Module 14: Time Series Forecasting

Introduction - Applications
Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques(Pattern based - Pattern less)
Basic Techniques - Averages, Smoothening, etc
Advanced Techniques - AR Models, ARIMA, etc
Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

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Data Science Online Training - FAQs

Q 1. Do I have to be a ThinkNEXT student to take data science online course?

Ans. No, our data science online training programs are open to everyone in India

Q 2. Who can join data science online courses at ThinkNEXT?

Ans. Students, professionals, freelancers, homepreneurs, entrepreneurs, irrespective of their diverse backgrounds, professions, and age groups, can join our data science online courses

Q 3. Are there any prerequisites or language requirements for data science online classes at ThinkNEXT?

Ans. There are no prerequisites. All courses are conducted in English and Hindi. For better retention, video lectures include English subtitles and the option to slow-down, pause, or replay lectures.

Q 4. Can I take more than one online course at a time at ThinkNEXT?

Ans. If you are new to online learning, we recommend that you begin with one course but you can always come back and continue your studies with one or more topics.

Q 5. Do I need specific equipment to take Data Science Online Courses at ThinkNEXT?

Ans. We strongly encourage you to have access to a desktop or laptop computer and reliable internet connection.