Branch - 100 Feet Road  Hopes  Kuniyamuthur

Branch - 100 Feet Road  Hopes  Kuniyamuthur

DATA SCIENTIST MASTER PROGRAMME

What you'll learn

Learning Objectives

Course content

  • Installation and Setting up path
    Features
  • Python variables
  • Input & Output and Import
  • Why Learn Python
  • Who used Python

Using Pycharm & Scripting Keywords & Identifiers Operators, Indentations

Basic Data types: int, float, string, Boolean and Complex Numbers and String Tuple and List Dictionary and Set Arrays

String, String slices Date Math Random and Statistics PDF Data Extraction CVS module

Introduction to Tuples Working with Tuples Tuple Operations Function and Methods

Introduction to Lists Working with lists Lists Operations Function and Methods

Introduction to Sets Working with Sets Sets Operations Function and Methods

If…else Elif For, for else, while Break and Continue Pass Looping Techniques

Types of Functions Function Arguments Recursion Anonymous Function Global, local and Nonlocal Lambda Functions Modules Packages

Reading & Writing Files Manipulating File Pointer Type of Files File Operations Directories File I/O Attributes, File Methods

Try, Except and Finally Try else Custom Exception Error Vs. Exception

Real-time in OOPS Access Specifiers Class and Objects Methods Overloading and Overriding Inheritance Abstraction and Data Hiding Properties &Self-keyword

OS module Environment variables, Paths, directories, and filenames. Working with file systems, Dates and times

Starting a New Thread Creating Thread Using Threading Module Synchronizing Threads Multithreaded Priority Queue

pattern matching and searching Real time parsing of networking or system data using regex validation Concepts

Web Scraping Chabot & Language Translate Find the Similarity ratio between text Tagging Sentence to find key word Google map integration Bubble sort algorithm QR code generator Spell checker Scraping Wikipedia Anagram & Screenshot app,Python for networking Getting input from user Working with images & building a photo gallery

Numpy Introduction, Numpy Arrays, Data Types in Numpy, Array Indexing, Array Slicing, Array Shape, Array Reshape, Array Iteration, Array Join, Array Split, Array Search & Sorting, Array Filter

Introduction Pandas, Series Pandas, Data Frames, Pandas Read CSV & JSON, Pandas Analyzing Data, Data Cleaning

Pandas Analyzing Data, Data Cleaning

Introduction, Pyplot Plotting, Markers, Line Labels, Grid, Subplot, Scatter Plot, Bar Chart, Histogram, Pie Chart

Introduction about Data Science, What is Data,Database Table
Central Tendency Max,Min,Mean Function, Mean,Median,Mode,Skewness ,Normal Distribution,: Probability Basics What does it mean by probability?, Types of Probability , ODDS Ratio?
Standard Deviaction Data deviation & distribution , Variance,Bias variance Tradeoff Underfitting , Overfitting
Distance metrics Euclidean Distance ,Manhattan Distance,Outlier analysis What is an Outlier? ,Inter Quartile Range,Box & whisker plot,Upper Whisker , Lower Whisker , Scatter plot, Cook’s Distance
:Missing Value treatment What is NA?,Central Imputation ,KNN imputation , Dummification,Correlation Correlation Coefficient ,Correlation Matrix ,Statistics Correlation vs Causality
Data Science Math Linear Functions,Plotting Linear Functions,Slope and Intercept,EDA Descriptive statistics,Grouping of data,Handling missing values in dataset,ANOVA-Analysis of variance,Correlation
Error Metric Classification : Confusion Matrix , Precision o Recall o Specificity o F1 Score,Error Metric Regression : MSE , RMSE ,MAPE
Machine Learning Overview : Introduction about Machine Learning, Data Set in ML ,Data Types in ML
Median , Mode , Percentile , Big Data Distribution , Normal Data Distribution
Linear Regression,Logistic Regression,Naive Bayes,Random forest,K-Nearest Neighbors,Support vector machine, Decision tree classification,Polynomial Regression,Multiple Regression, Scale,Grid Search, AUC-ROC Curve ,Confusion Matrix,Categorical Data,Bootstrap Aggregation,Cross Validation,Natural Language Processing
Hierarchical Clustering, K-Means , K-Means++

FORMULAS & FUNCTIONS, Aggregate Function, Logical Function, Lookup & References, Financial Functions, Formatting and Proofing

Conditional Formatting using New Rule Conditional Formatting using Formula

Creating Simple Pivot Tables, Classic Pivot table Basic and Advanced Value Field Setting, Calculated Field & Calculated Items, Grouping based on numbers and Dates

Activating Power pivot Usage of Data model DAX Calculation Relational Data

Using SLICERS, Filter data with Slicers Various Charts i.e. Bar Charts/Pie Charts/Line Charts Manage Primary and Secondary Axis

Number, Date & Time Validation Text and List Validation Custom Validation Dropdown List Validation.

Creating Scenarios, Working with Data Tables, Using Goal Seek, Using Solver, Using Consolidating Data by Position or Category, Consolidating Data Using Formulas Excel

Macro Builder, Create a standalone macro, Create an embedded macro, Add actions to a macro, Control program flow with If, Else If, and Else, Create sub macros, Group related actions together, Expand and collapse macro actions or blocks, Copy and paste macro actions, Share a macro with others

Get started with database, SQL and MySQL What is database? Why use SQL? Importance of MySQL

First Steps in SQL, Creating a database, Introduction to data types, Creating a table

Get acquainted with the interface

DDL DCL DML TCL

Relational Database essentials, Primary key, Foreign key, Unique key and null values

Load the database, Loading employees’ database, Starting with SELECT statement, Select-From Where And-Or (In-not in) Like-not like, Wildcard characters Between-and Is not null-is null, Select distinct, Aggregate statement, Order by-Group by Using Aliases, Having and Limit

Insert statement, Inserting data INTO table, Update statement, Commit and rollback, Delete statement, Drop vs Truncate, AGGREGATE FUNCTIONS, Functions Count Sum Min() and Max Avg Round()

What is Power BI, Why we have to use?

Components

Data Sources : Excel, SQL, CSV, JSON, Access

Power BI vs Tableau

Using Data Modeling and Navigation, Creating Calculated, Columns, Creating Calculated Tables, Managing Time-Based Data

Exploring Different Datasets, Creating and Sharing Dashboards, Tiles in Dashboard, Data Gateway

Creating Simple Visualizations, Creating Map Visualizations, Using Combination Charts, Using Tables, Modify Colors in Charts, Adding Shapes, Images and Text box, Styling Reports, Duplicating Reports

Using Excel Data Importing xlsx Files

Using Power BI Desktop for Report Sharing, Printing Power BI Dashboards, Export Options, Publishing Report to Web, Using & Editing Content Pack

DAX Architecture, Entity Sets DAX Data Types, Syntax Rules DAX Measures and Calculation Data Modeling Options in DAX

Purchasing REST API Security

BI Concepts What is TABLEAU? Why Data Visualization? Unique Features compared to Traditional BI Tools TABLEAU Overview & Architecture File Types & Extensions

Data Connections in the Tableau Interface, Connecting to Tableau, Data Server, Types of Join, When to Use Joining, What is Data Blending, When to Use Data Blending, Joining vs. Blending, Creating Data Extracts in Tableau, Shadow Extracts, Prepare your Data for Analysis

Filters. Applying Filters Quick Filters Sorting of Data Creating Combined Fields Creating Groups and Defining Aliases Working with Sets and Combined Sets Drill to Other Levels in a Hierarchy Grand totals and Subtotals Tableau Bins Fixed Sized Bins Variable Sized Bins Creating and using Parameters Exploring Parameter Controls Using parameters for titles, field selections, logic statements, Top X Cross Tabs [Pivot Tables] Page Trials Total and Sub-Total

Working with Combined Axis Working with Combination Charts Working with Geocoding and Geographic Mapping Using Scatter Plots Using Text tables and Highlight tables Using Heat Maps Using Histograms Using Pie Charts Using Bullet Charts

Using Pareto Charts Using Waterfall Charts Using Gantt Charts Using Box Plots Using Sparkline Charts Using Density Charts Using KPI Charts Small Multiples Working with aggregate versus disaggregate data What is Market Basket Analysis Performing Market Basket Analysis

Formatting Options in Formatting Visualizations Working with Labels and Annotations Effective Use of Titles and Captions Introduction to Visual Best Practices

Importing and Modifying Custom Geocoding Working with Symbol Map and Filled Map Using Background Image Exploring Geographic Search Perform Pan Zoom Lasso and Radial Selection Working with WMS Server Maps [Web Map Service]

Add Reference Lines Bands and Distribution Adding Reference Lines Adding Reference Bands Adding Reference Distribution Working Reference Lines Bands and Forecasting Trend lines and Trend Models

Build Interactive Dashboards Best practices for creating effective dashboards Creating a Dashboard and Importing Sheets Interaction Exploring Dashboard Actions Use of Running Actions & Dashboard Actions How to Share your Reports & Exporting your Work

Description

Become a Skilled Data Analyst with Industry-Leading Tools. This program provides comprehensive training in data analytics, from fundamentals to advanced techniques. Learn to work with data visualization, statistical analysis, and machine learning using industry-standard tools. Gain hands-on experience in handling real-world data, uncovering insights, and making data-driven decisions. Enhance your expertise and improve your career prospects in the growing field of data analytics.

Mapped Certificate

Google Flutter Certification

via Google Developer's Certification Program

MongoDB University

For Flutter + Firebase integration

Lectures

7 Modules with Certificates

Duration

5 Months

Language

English, Tamil

Mode

Online, Offline

Learn Smarter, Not Hearder

Enjoyable Learning
Experience Awiat You

Our courses are taught by experienced professionals and subject matter experts who are passionate.

10,000

Daily Active Users

60%

Courses Enrollment Rate

4.7

Ratings

by 12 Learners

Frequently Asked Questions

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What is the Python course?
Python course will empower learners to build dynamic applications by mastering syntax, data structures, and object-oriented programming. With its vast ecosystem of libraries and frameworks, Python is essential for web development, data science, automation, and AI — making it a must-have skill for future-ready developers.

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