Python Course for Data Science in Kolkata


Python is one of the most popular programming languages and has been gaining popularity in the data science community in recent years. Python course for Data Science Course in Kolkata is designed to give you an in-depth understanding of the various libraries and frameworks used in data science. The course covers various topics such as data wrangling, data visualization, machine learning, and deep learning. The course also provides placement assistance to help you get started with your data science career. If you want to do Python course for Data Science in Kolkata, you are at right place.
Acesoftech Academy offers advanced python course which comes with projects so that you can get in-depth knowledge about python. If you are looking for Python training institute for Data Science in Kolkata, you are assured to provide professional training.


    Python for Data Science Course Syllabus

    Introduction to Statistical Analysis

    • Counting, Probability, and Probability Distributions
    • Sampling Distributions
    • Estimation and Hypothesis Testing
    • Scatter Diagram
    • Anova and Chisquare
    • Imputation Techniques
    • Data Cleaning
    • Correlation and Regression

    Introduction to Data Analytics

    • Data Analytics Overview
    • Importance of Data Analytics
    • Types of Data Analytics
    • Descriptive Analytics
    • Diagnostic Analytics
    • Predictive Analytics
    • Prescriptive Analytics
    • Benefits of Data Analytics
    • Data Visualization for Decision Making
    • Data Types, Measure Of central tendency, Measures of Dispersion
    • Graphical Techniques, Skewness & Kurtosis, Box Plot
    • Descriptive Stats
    • Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval

    • Excel tutorial
    • Text to Columns
    • Concatenate
    • The Concatenate Function
    • The Right Function with Concatenation
    • Absolute Cell References
    • Data Validation
    • Time and Date Calculations
    • Conditional Formatting
    • Exploring Styles and Clearing Formatting
    • Using Conditional Formatting to Hide Cells
    • Using the IF Function
    • Changing the “Value if false” Condition to Text
    • Pivot Tables
    • Creating a Pivot Table
    • Specifying PivotTable Data
    • Changing a PivotTables Calculation
    • Filtering and Sorting a PivotTable
    • Creating a PivotChart
    • Grouping Items
    • Updating a PivotTable
    • Formatting a PivotTable
    • Using Slicers
    • Charts
    • Creating a Simple Chart
    • Charting Non-Adjacent Cells
    • Creating a Chart Using the Chart Wizard
    • Modifying Charts
    • Moving an Embedded Chart
    • Sizing an Embedded Chart
    • Changing the Chart Type
    • Chart Types
    • Changing the Way Data is Displayed
    • Moving the Legend
    • Formatting Charts
    • Adding Chart Items
    • Formatting All Text
    • Formatting and Aligning Numbers
    • Formatting the Plot Area
    • Formatting Data Markers
    • Pie Charts
    • Creating a Pie Chart
    • Moving the Pie Chart to its Own Sheet
    • Adding Data Labels
    • Exploding a Slice of a Pie Chart
    • Data Analysis − Overview
    • types of Data Analysis
    • Data Analysis Process
    • Working with Range Names
    • Copying Name using Formula Autocomplete
    • Range Name Syntax Rules
    • Creating Range Names
    • Creating Names for Constants
    • Managing Names
    • Scope of a Name
    • Editing Names
    • Applying Names
    • Using Names in a Formula
    • Viewing Names in a Workbook
    • Copying Formulas with Names
    • Difference between Tables and Ranges
    • Create Table
    • Table Name
    • Managing Names in a Table
    • Table Headers replacing Column Letters
    • Propagation of a Formula in a Table
    • Resize Table
    • Remove Duplicates
    • Convert to Range
    • Table Style Options
    • Table Styles
    • Cleaning Data with Text Functions
    • Removing Unwanted Characters from Text
    • Extracting Data Values from Text
    • Formatting Data with Text Functions
    • Date Formats
    • Conditional Formatting
    • Sorting
    • Filtering
    • Lookup Functions
    • Pivoting

    • Introduction to Oracle Database
    • Retrieve Data using the SQL SELECT Statement
    • Learn to Restrict and Sort Data
    • Usage of Single-Row Functions to Customize Output
    • Invoke Conversion Functions and Conditional Expressions
    • Aggregate Data Using the Group Functions
    • Display Data from Multiple Tables Using Joins
    • Use Sub-Queries to Solve Queries
    • The SET Operators
    • Data Manipulation Statements
    • Use of DDL Statements to Create and Manage Tables
    • Other Schema Objects
    • Control User Access
    • Management of Schema Objects
    • Manage Objects with Data Dictionary Views
    • Manipulate Large Data Sets
    • Data Management in Different Time Zones
    • Retrieve Data Using Sub-queries
    • Regular Expression Support

    Module 1: Tableau Course Material

    • Start Page
    • Show Me
    • Connecting to Excel Files
    • Connecting to Text Files
    • Connect to Microsoft SQL Server
    • Connecting to Microsoft Analysis Services
    • Creating and Removing Hierarchies
    • Bins
    • Joining Tables
    • Data Blending

    Module 2: Learn Tableau Basic Reports

    • Parameters
    • Grouping Example 1
    • Grouping Example 2
    • Edit Groups
    • Set
    • Combined Sets
    • Creating a First Report
    • Data Labels
    • Create Folders
    • Sorting Data
    • Add Totals, Sub Totals and Grand Totals to Report

    Module 3: Learn Tableau Charts

    • Area Chart
    • Bar Chart
    • Box Plot
    • Bubble Chart
    • Bump Chart
    • Bullet Graph
    • Circle Views
    • Dual Combination Chart
    • Dual Lines Chart
    • Funnel Chart
    • Traditional Funnel Charts
    • Gantt Chart
    • Grouped Bar or Side by Side Bars Chart
    • Heatmap
    • Highlight Table
    • Histogram
    • Cumulative Histogram
    • Line Chart
    • Lollipop Chart
    • Pareto Chart
    • Pie Chart
    • Scatter Plot
    • Stacked Bar Chart
    • Text Label
    • Tree Map
    • Word Cloud
    • Waterfall Chart

    Module 4: Learn Tableau Advanced Reports

    • Dual Axis Reports
    • Blended Axis
    • Individual Axis
    • Add Reference Lines
    • Reference Bands
    • Reference Distributions
    • Basic Maps
    • Symbol Map
    • Use Google Maps
    • Mapbox Maps as a Background Map
    • WMS Server Map as a Background Map

    Module 5: Learn Tableau Calculations & Filters

    • Calculated Fields
    • Basic Approach to Calculate Rank
    • Advanced Approach to Calculate Ra
    • Calculating Running Total
    • Filters Introduction
    • Quick Filters
    • Filters on Dimensions
    • Conditional Filters
    • Top and Bottom Filters
    • Filters on Measures
    • Context Filters
    • Slicing Fliters
    • Data Source Filters
    • Extract Filters

    Module 6: Learn Tableau Dashboards

    • Create a Dashboard
    • Format Dashboard Layou
    • Create a Device Preview of a Dashboard
    • Create Filters on Dashboard
    • Dashboard Objects
    • Create a Story

    Module 7: Server

    • Tableau online.
    • Overview of Tableau Server.
    • Publishing Tableau objects and scheduling/subscription.

    Module 1: Introduction to Power BI

    • Get Started with Power BI
    • Overview: Power BI concepts
    • Sign up for Power BI
    • Overview: Power BI data sources
    • Connect to a SaaS solution
    • Upload a local CSV file
    • Connect to Excel data that can be refreshed
    • Connect to a sample
    • Create a Report with Visualizations
    • Explore the Power BI portal

    Module 2: Viz and Tiles

    • Overview: Visualizations
    • Using visualizations
    • Create a new report
    • Create and arrange visualizations
    • Format a visualization
    • Create chart visualizations
    • Use text, map, and gauge visualizations and save a report
    • Use a slicer to filter visualizations
    • Sort, copy, and paste visualizations
    • Download and use a custom visual from the gallery

    Module 3: Reports and Dashboards

    • Modify and Print a Report
    • Rename and delete report pages
    • Add a filter to a page or report
    • Set visualization interactions
    • Print a report page
    • Send a report to PowerPoint
    • Create a Dashboard
    • Create and manage dashboards
    • Pin a report tile to a dashboard
    • Pin a live report page to a dashboard
    • Pin a tile from another dashboard
    • Pin an Excel element to a dashboard
    • Manage pinned elements in Excel
    • Add a tile to a dashboard
    • Build a dashboard with Quick Insights
    • Set a Featured (default) dashboard
    • Ask Questions about Your Data
    • Ask a question with Power BI Q&A
    • Tweak your dataset for Q&A
    • Enable Cortana for Power BI

    Module 4: Publishing Workbooks and Workspace

    • Share Data with Colleagues and Others
    • Publish a report to the web
    • Manage published reports
    • Share a dashboard
    • Create an app workspace and add users
    • Use an app workspace
    • Publish an app
    • Create a QR code to share a tile
    • Embed a report in SharePoint Online

    Module 5: Other Power BI Components and Table Relationship

    • Use Power BI Mobile Apps
    • Get Power BI for mobile
    • View reports and dashboards in the iPad app
    • Use workspaces in the mobile app
    • Sharing from Power BI Mobile
    • Use Power BI Desktop
    • Install and launch Power BI Desktop
    • Get data
    • Reduce data
    • Transform data
    • Relate tables
    • Get Power BI Desktop data with the Power BI service
    • Export a report from Power BI service to Desktop

    Module 6: DAX functions

    • New Dax functions
    • Date and time functions
    • Time intelligence functions
    • Filter functions
    • Information functions
    • Logical functions
    • Math & trig functions
    • Parent and child functions
    • Text functions

    Why is Python Essential for Data Science?

    The following are a few reasons why Python is an essential tool for data science:

    Python is Flexible: In terms of flexibility, Python is ideal for developers who wish to script applications and websites. This is because Python is a simple language that does not require as much coding skill as other languages.

    Easy to Learn: The main goal of Python is to keep things simple and readable, making it an excellent tool for beginners. Furthermore, Python has a relatively low learning curve, giving programmers more freedom to experiment. As compared to the old languages, Python has fewer lines of code, so programmers can play around more.

    Python is Open Source: Essentially, Python is free and designed to run on Windows and Linux environments. It can also be ported to other platforms as well.

    Large community: Python is widely used in academia and industry. This means there are many analytics libraries available. Additionally, Python users have access to Stack Overflow, mailing lists, and user-contributed code and documentation. Python is becoming more and more popular every day.

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    Why learn Python Course for Data Science?

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