Welcome to Hindustan Study - Education, Research & Training

Data Analytics


4.5 Star Rating: Very Good 4.5 out of 5 based on 1699 Votes.

Are you Looking for the Best Institute for Data Analytics using Python training in Darbhanga,Bihar? HINDUSTAN STUDY offers Data Analytics using Python training classes with live project by expert trainer in Darbhanga,bihar. Our Data Analytics using Python training program in Darbhanga,Bihar is specially designed for Under-Graduates (UG), Graduates, working professional and also for Freelancers. We provide end to end learning on Data Analytics using Python Domain with deeper dives for creating a winning career for every profile.

Why To Enroll In Our Data Analytics Using Python Training Course in Darbhanga,Bihar?

We Focus on Innovative ideas, High-quality Training, Smart Classes, 100% job assistance, Opening the doors of opportunities. Our Data Analytics using Python Trainees are working across the nation. We at Hindustan Study India, No#1 Data Analytics using Python Course in Darbhanga,Bihar with 100% Placement. Certified Trainers with Over 10,000 Students Trained in Data Analytics using Python Course in Darbhanga,Bihar.

What Our Students Will Get During Data Analytics using Python Training Course?

Get dedicated student support, career services, industry expert mentors and real world projects. Career Counselling. Timely Doubt Resolution. 50% Salary Hike, Career Counselling Case Studies + Tools + Certificate.

Why learn Data Analytics using Python?

It’s continued to be a great option for data scientists who use it for building Machine learning applications or and using them and other scientific computations. Data Analytics Using Python Training in Darbhanga,Bihar cuts development time in half with its simple to read syntax and easy compilation feature with easy to learn the concept. Debugging any type of programs is a breeze in this language with its built-in debugger. It runs on every famous type of platforms like Windows, Linux/Unix, and Mac OS and has been ported to Java and .NET virtual machines. Python is free to use language, even for commercial products, because of its OSI-approved open source license, so anyone can use it for free. It has opted as the most preferred Language for Data Analytics and the increasing search trends on Python every day also indicates that it is the “Next Big Thing” and a must for aspirants in the Data Analytics field.


Why Hindustan Study?

Hindustan Study has a dedicated team of highly expert trainers to identify, evaluate, implement and providing Best Data Analytics Using Python Training Institute in Darbhanga,Bihar for our students. Our Trainers leverage a defined methodology that helps identify opportunity, develop the most optimal resolution and maturely execute the solution. We have the best trainers across the world to provide Best Data Analytics Using Python Training in Darbhanga,Bihar who are highly qualified and are best in their field.

The Training & Placement cell is committed to providing all attainable help to the students in their efforts to seek out employment and internships in every field. The placement department works beside alternative departments as a team in molding the scholars to the necessities of varied industries. We got proactive and business clued-in Placement Cells that pride itself on a robust skilled network across numerous sectors. It actively coordinates with every student and ensures that they get placed with purported MNCs among six months of graduating. We are the Best Data Analytics Using Python Training Institute in Darbhanga,Bihar.

  • Introduction To Python

    • Why Python
    • Application areas of python
    • Python implementations
    • Cpython
    • Jython
    • Ironpython
    • Pypy
    • Python versions
    • Installing python
    • Python interpreter architecture
    • Python byte code compiler
    • Python virtual machine(pvm)

    Writing and Executing First Python Program

    • Using interactive mode
    • Using script mode
    • General text editor and command window
    • Idle editor and idle shell
    • Understanding print() function
    • How to compile python program explicitly

    Python Language Fundamentals

    • Character set
    • Keywords
    • Comments
    • Variables
    • Literals
    • Operators
    • Reading input from console
    • Parsing string to int, float

    Python Conditional Statements

    • If statement
    • If else statement
    • If elif statement
    • If elif else statement
    • Nested if statement

    Looping Statements

    • While loop
    • For loop
    • Nested loops
    • Pass, break and continue keywords

    Standard Data Types

    • Int, float, complex, bool, nonetype
    • Str, list, tuple, range
    • Dict, set, frozenset

    String Handling

    • What is string
    • String representations
    • Unicode string
    • String functions, methods
    • String indexing and slicing
    • String formatting

    Python List

    • Creating and accessing lists
    • Indexing and slicing lists
    • List methods
    • Nested lists
    • List comprehension

    Python Tuple

    • Creating tuple
    • Accessing tuple
    • Immutability of tuple

    Python Set

    • How to create a set
    • Iteration over sets
    • Python set methods
    • Python frozenset

    Python Dictionary

    • Creating a dictionary
    • Dictionary methods
    • Accessing values from dictionary
    • Updating dictionary
    • Iterating dictionary
    • Dictionary comprehension

    Python Functions

    • Defining a function
    • Calling a function
    • Types of functions
    • Function arguments
    • Positional arguments, keyword arguments
    • Default arguments, non-default arguments
    • Arbitrary arguments, keyword arbitrary arguments
    • Function return statement
    • Nested function
    • Function as argument
    • Function as return statement
    • Decorator function
    • Closure
    • Map(), filter(), reduce(), any() functions
    • Anonymous or lambda function

    Modules & Packages

    • Why modules
    • Script v/s module
    • Importing module
    • Standard v/s third party modules
    • Why packages
    • Understanding pip utility

    File I/O

    • Introduction to file handling
    • File modes
    • Functions and methods related to file handling
    • Understanding with block

    Regular Expressions(Regex)

    • Need of regular expressions
    • Re module
    • Functions /methods related to regex
    • Meta characters & special sequences


    Introduction to Database

    • Database Concepts
    • What is Database Package?
    • Understanding Data Storage
    • Relational Database (RDBMS) Concept

    SQL (Structured Query Language)

    • SQL basics
    • DML, DDL & DQL
    • DDL: create, alter, drop
    • SQL constraints:
    • Not null, unique
    • ,
    • Primary & foreign key, composite key
    • Check, default
    • DML: insert, update, delete and merge
    • DQL : select
    • Select distinct
    • SQL where
    • SQL operators
    • SQL like
    • SQL order by
    • SQL aliases
    • SQL views
    • SQL joins
    • Inner join
    • Left (outer) join
    • Right (outer) join
    • Full (outer) join
    • Mysql functions
    • String functions
    • Char_length
    • Concat
    • Lowe
    • r
    • Reverse
    • Uppe
    • r
    • Numeric functions
    • Max, min, sum
    • Avg, count, abs
    • Date functions
    • Curdate
    • Curtime●
    • Now

    Statistics, Probability &Analytics:

    Introduction to Statistics

    • Sample or population
    • Measures of central tendency
    • Arithmetic mean
    • Harmonic mean
    • Geometric mean
    • Mode
    • Quartile
    • First quartile
    • Second quartile(median)
    • Third quartile
    • Standard deviation

    Probability Distributions

    • Introduction to probability
    • Conditional probability
    • Normal distribution
    • Uniform distribution
    • Exponential distribution
    • Right & left skewed distribution
    • Random distribution
    • Central limit theorem

    Hypothesis Testing

    • Normality test
    • Mean test
    • T-test
    • Z-test
    • ANOVA test
    • Chi square test
    • Correlation and covariance

    Numpy Package

    • Difference between list and numpy array
    • Vector and matrix operations
    • Array indexing and slicing

    Pandas Package

    Introduction to pandas

    • Labeled and structured data
    • Series and dataframe objects

    How to load datasets

    • From excel
    • From csv
    • From html table

    Accessing data from Data Frame

    • at &iat
    • loc&iloc
    • head() & tail()

    Exploratory Data Analysis (EDA)

    • describe()
    • groupby()
    • crosstab()
    • boolean slicing / query

    Data Manipulation & Cleaning

    • Map(), apply()
    • Combining data frames
    • Adding/removing rows & columns
    • Sorting data
    • Handling missing values
    • Handling duplicacy
    • Handling data error

    Handling Date and Time

    Data Visualization using matplotlib and seaborn packages

    • Scatter plot, lineplot, bar plot
    • Histogram, pie chart,
    • Jointplot, pairplot, heatmap
    • Outlier detection using boxplot

    Advanced Excel

    Advanced Excel Course – Overview of the Basics of Excel

    • Customizing common options in Excel
    • Absolute and relative cells
    • Protecting and un-protecting worksheets and cells

    – Working with Functions

    • Writing conditional expressions (using IF)
    • Using logical functions (AND, OR, NOT)
    • Using lookup and reference functions (VLOOKUP, HLOOKUP, MATCH, INDEX)
    • VlookUP with Exact Match, Approximate Match
    • Nested VlookUP with Exact Match
    • VlookUP with Tables, Dynamic Ranges
    • Nested VlookUP with Exact Match
    • Using VLookUP to consolidate Data from Multiple Sheets

    Advanced Excel Course – Data Validations

    • Specifying a valid range of values for a cell
    • Specifying a list of valid values for a cell
    • Specifying custom validations based on formula for a cell

    Advanced Excel Course – Working with Templates

    • Designing the structure of a template
    • Using templates for standardization of worksheets

    Advanced Excel Course – Sorting and Filtering Data

    • Sorting tables
    • Using multiple-level sorting
    • Using custom sorting
    • Filtering data for selected view (AutoFilter)
    • Using advanced filter options

    Advanced Excel Course – Working with Reports

    • Creating subtotals
    • Multiple-level subtotals
    • Creating Pivot tables
    • Formatting and customizing Pivot tables
    • Using advanced options of Pivot tables
    • Pivot charts
    • Consolidating data from multiple sheets and files using Pivot tables
    • Using external data sources
    • Using data consolidation feature to consolidate data
    • Show Value As ( % of Row, % of Column, Running Total, Compare with Specific Field)
    • Viewing Subtotal under Pivot
    • Creating Slicers ( Version 2010 & Above)

    Advanced Excel Course – More Functions

    • Date and time functions
    • Text functions
    • Database functions
    • Power Functions (CountIf, CountIFS, SumIF, SumIfS)

    Advanced Excel Course – Formatting

    • Using auto formatting option for worksheets
    • Using conditional formatting option for rows, columns and cells

    Advanced Excel Course – Macros

    • Relative & Absolute Macros
    • Editing Macro’s

    Advanced Excel Course – WhatIf Analysis

    • Goal Seek
    • Data Tables
    • Scenario Manager

    Advanced Excel Course – Charts

    • Using Charts
    • Formatting Charts
    • Using 3D Graphs
    • Using Bar and Line Chart together
    • Using Secondary Axis in Graphs
    • Sharing Charts with PowerPoint / MS Word, Dynamically
    • (Data Modified in Excel, Chart would automatically get updated)

    Advanced Excel Course – New Features Of Excel

    • Sparklines, Inline Charts, data Charts
    • Overview of all the new features

    Advanced Excel Course – Final Assignment

    • The Final Assignment would test contains questions to be solved at the end of the Course


    Create a Macro:

    • Swap Values, Run Code from a Module, Macro Recorder, Use Relative References,
    • FormulaR1C1, Add a Macro to the Toolbar, Macro Security, Protect Macro.


    • MsgBox Function, Input Box Function.

    Workbook and Worksheet Object:

    • Path and Full Name, Close and Open, Loop through Books and Sheets, Sales Calculator, Files in a Directory, Import Sheets, Programming Charts.

    Range Object:

    • Current Region, Dynamic Range, Resize, Entire Rows and Columns, Offset, From Active Cell to Last Entry, Union and Intersect, Test a Selection, Possible Football Matches, Font, Background Colors, Areas Collection, Compare Ranges.


    • Option Explicit, Variable Scope, Life of Variables.

    If Then Statement:

    • Logical Operators, Select Case, Tax Rates, Mod Operator, Prime Number Checker, Find Second Highest Value, Sum by Color, Delete Blank Cells.


    • Loop through Defined Range, Loop through Entire Column, Do Until Loop, Step Keyword, Create a Pattern, Sort Numbers, Randomly Sort Data, Remove Duplicates, Complex Calculations, Knapsack Problem.

    Macro Errors:

    • Debugging, Error Handling, Err Object, Interrupt a Macro, Macro Comments.

    String Manipulation:

    • Separate Strings, Reverse Strings, Convert to Proper Case, Count Words.

    Date and Time:

    • Compare Dates and Times, DateDif Function, Weekdays, Delay a Macro, Year Occurrences, Tasks on Schedule, Sort Birthdays.


    • Before DoubleClick Event, Highlight Active Cell, Create a Footer Before Printing, Bills and Coins, Rolling Average Table
    • .


    • Dynamic Array, Array Function, Month Names, Size of an Array.

    Function and Sub:

    • User Defined Function, Custom Average Function, Volatile Functions, ByRef and ByVal.

    Application Object:

    • Status Bar, Read Data from Text File, Write Data to Text File.

    ActiveX Controls:

    • Text Box, List Box, Combo Box, Check Box, Option Buttons, Spin Button, Loan Calculator.

    User form:

    • User form and Ranges, Currency Converter, Progress Indicator, Multiple List Box Selections, Multicolumn Combo Box, Dependent Combo Boxes, Loop through Controls, Controls Collection, User form with Multiple Pages, Interactive User form


    Tableau – Home

    • Tableau – overview
    • Tableau – environment setup
    • Tableau – get started
    • Tableau – navigation
    • Tableau – design flow
    • Tableau – file types
    • Tableau – data types
    • Tableau – show me
    • Tableau – data terminology

    Tableau – Data Sources

    • Tableau – custom data view
    • Tableau – data sources
    • Tableau – extracting data
    • Tableau – fields operations
    • Tableau – editing metadata
    • Tableau – data joining
    • Tableau – data blending

    Tableau – Work Sheet

    • Tableau – add worksheets
    • Tableau – rename worksheet
    • Tableau – save & delete worksheet
    • Tableau – reorder worksheet
    • Tableau – paged workbook

    Tableau – Calculation

    • Tableau – operators
    • Tableau – functions
    • Tableau – numeric calculations
    • Tableau – string calculations
    • Tableau – date calculations
    • Tableau – table calculations
    • Tableau – lod expressions

    Tableau – Sorting & Filter

    • Tableau – basic sorting
    • Tableau – basic filters
    • Tableau – quick filters
    • Tableau – context filters
    • Tableau – condition filters
    • Tableau – top filters
    • Tableau – filter operations

    Tableau – Charts

    • Tableau – bar chart
    • Tableau – line chart
    • Tableau – pie chart
    • Tableau – crosstab
    • Tableau – scatter plot
    • Tableau – bubble chart
    • Tableau – bullet graph
    • Tableau – box plot
    • Tableau – tree map
    • Tableau – bump chart
    • Tableau – gantt chart
    • Tableau – histogram
    • Tableau – motion charts
    • Tableau – waterfall charts
    • Tableau – dashboard


    • One project using python &sql
    • One dashboard using tableau
Scroll to top