Python Data Analytics Training for Analysis and Visualization

Python Data Analytics training examines how Python turns raw datasets into useful business evidence. The course focuses on data cleaning, exploratory analysis, transformation, and data visualization, using Python workflows to identify patterns, measure performance, and communicate findings clearly. Learners work with the analytical techniques behind data-driven decision-making, building practical confidence in preparing data and presenting results for real operational questions.
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Curriculum

Python Data Analytics, ZeblearnIndia, Data Visualization, Python training, Data-driven decision-making Course Syllabus Structure

The curriculum covers data cleaning, exploratory analysis, and predictive modeling with Python. You will build dashboards using Plotly and automate reports with Jupyter notebooks.

  • Introduction to the Course
  • macOS - Download and Install the Anaconda Distribution
  • Windows - Download and Install the Anaconda Distribution
  • How to Uninstall the Anaconda Distribution
  • Use Anaconda Navigator to Create a New Environment
  • Download Course Materials
  • Unpack Course Materials + The Startdown and Shutdown Process
  • Intro to the Jupyter Lab Interface
  • Code Cell Execution
  • Import Libraries into Jupyter Lab
  • Installation and Setup

  • Comments
  • Basic Data Types
  • Operators
  • Variables
  • Declare Variables
  • Built-in Functions
  • Custom Functions
  • String Methods
  • Lists
  • Creating Lists
  • Index Positions and Slicing
  • Dictionaries
  • Creating Dictionaries
  • Classes
  • Navigating Libraries using Jupyter Lab
  • Python Crash Course

  • Create a Series Object from a List
  • Create a Series Object from a Dictionary
  • Intro to Series Methods
  • Intro to Attributes
  • Attributes and Methods on a Series
  • Parameters and Arguments
  • Import Series with the pd.read_csv Function
  • The head and tail Methods
  • Passing Series to Python Built-In Functions
  • Check for Inclusion with Python's in Keyword
  • The sort_values Method
  • The sort_index Method
  • Extract Series Values by Index Position
  • Extract Series Values by Index Label
  • The get Method
  • Overwrite a Series Value
  • The copy Method
  • Math Methods on Series Objects
  • Broadcasting
  • The value_counts Method
  • The apply Method
  • The map Method
  • Series

  • Methods and Attributes between Series and DataFrames
  • Select One Column from a DataFrame
  • Select Multiple Columns from a DataFrame
  • Add New Column to DataFrame
  • A Review of the value_counts Method
  • Drop DataFrame Rows with Missing Values
  • Fill in Missing Values with the fillna Method
  • The astype Method 
  • Sort a DataFrame with the sort_values Method 
  • Sort DataFrame with the sort_index Method
  • Rank Series Values with the rank Method
  • DataFrames

  • This Module's Dataset + Memory Optimization
  • Filter a DataFrame Based on a Condition
  • Filter with More than One Condition (AND - &)
  • Filter with More than One Condition (OR - |)
  • The isin Method
  • The isnull and notnull Methods
  • The between Method
  • The duplicated Method
  • The drop_duplicates Method
  • The unique and nunique Methods

  • This Module's Dataset
  • The set_index and reset_index Methods
  • Retrieve Rows by Index Position with iloc Accessor
  • Retrieve Rows by Index Label with loc Accessor
  • Second Arguments to loc and iloc Accessors
  • Overwrite Value in a DataFrame
  • Overwrite Multiple Values in a DataFrame
  • Rename Index Labels or Columns in a DataFrame
  • Delete Rows or Columns from a DataFrame
  • Create Random Sample with the sample Method
  • The nsmallest and nlargest Methods
  • Filtering with the where Method
  • The apply Method with DataFrames

  • This Module's Dataset
  • Common String Methods
  • Filtering with String Methods
  • String Methods on Index and Columns
  • The split Method
  • More Practice with Splits
  • The expand and n Parameters of the split Method

  • Intro to the MultiIndex Module
  • Create a MultiIndex
  • Extract Index Level Values
  • Rename Index Labels
  • The sort_index Method on a MultiIndex DataFrame
  • Extract Rows from a MultiIndex DataFrame
  • The transpose Method
  • The stack Method
  • The unstack Method
  • The pivot Method
  • The melt Method
  • The pivot_table Method

  • Intro to the GroupBy Module
  • The groupby Method
  • Retrieve A Group with the get_group Method
  • Methods on the GroupBy Object
  • Grouping by Multiple Columns
  • The agg Method
  • Iterating through Groups

  • Intro to the Merging DataFrames Module
  • The pd.concat Function I
  • The pd.concat Function II
  • Left Joins
  • The left_on and right_on Parameters
  • Inner Joins I
  • Inner Joins II
  • Full-Outer Joins
  • Merging by Indexes with the left_index and right_index Parameters
  • The join Method

  • Intro to the Working with Dates and Times Module and Review of Python's datetime
  • The Timestamp and DatetimeIndex Objects
  • Create Range of Dates with pd.date_range Function
  • The dt Attribute
  • Selecting Rows from a DataFrame with DatetimeIndex
  • The DateOffset Object
  • Specialized Date Offsets
  • Timedeltas

  • URL for Next Lesson's Dataset
  • Intro to the Input and Output Module
  • Export DataFrame to CSV File
  • Install openpyxl Library to Read and Write Excel Files
  • Import Excel File into pandas
  • Export Excel File from pandas

  • Install matplotlib Library for Visualization
  • The plot Method
  • Modifying Plot Aesthetics with Templates
  • Bar Charts
  • Pie Charts

  • Introduction to the Options and Settings Module
  • Changing Options with Attributes
  • Changing Options with Functions
  • The precision Option

  • Overview of BI Concepts
  • Why We Need BI
  • Introduction to SSBI
  • SSBI Tools
  • Why Power BI?
  • What is Power BI?
  • Building Blocks of Power BI
  • Getting Started with Power BI Desktop
  • Get Power BI Tools
  • Introduction to Tools and Terminology
  • Dashboard in Minutes
  • Refreshing Power BI Service Data
  • Interacting with Your Dashboards
  • Sharing Dashboards and Reports

  • How to Use Visuals in Power BI
  • What Are Custom Visuals?
  • Creating Visualizations and Color Formatting
  • Setting Sort Order
  • Scatter & Bubble Charts & Play Axis
  • Tooltips and Slicers, Timeline Slicers & Sync Slicers
  • Cross Filtering and Highlighting
  • Visual, Page, and Report Level Filters
  • Drill Down/Up
  • Hierarchies and Reference/Constant Lines
  • Tables, Matrices & Conditional Formatting
  • KPIs, Cards & Gauges
  • Map Visualizations
  • Managing and Arranging Visuals
  • Drill Through and Custom Report Themes

  • What Is Data Visualization?
  • Comparison and Benefits Against Reading Raw Numbers
  • Real Use Cases from Various Business Domains
  • Quick and Powerful Examples Using Tableau (Without Technical Details)
  • Installing Tableau
  • Tableau Interface
  • Connecting to Data Source
  • Tableau Data Types
  • Data Preparation

  • Installation of Tableau Desktop
  • Architecture of Tableau
  • Interface of Tableau
  • Layout
  • Toolbars
  • Data Pane
  • Analytics Pane
  • How to Start with Tableau
  • Ways to Share and Export Work Done in Tableau

  • Connection to Excel
  • Connection to Cubes and PDFs
  • Management of Metadata and Extracts
  • Data Preparation
  • Joins
  • Union
  • Dealing with NULL Values
  • Cross-Database Joining
  • Data Extraction
  • Data Blending

  • Filters
  • Interactive Filters
  • Creating Folders in Tableau
  • Sorting in Tableau
  • Types of Filters
  • Filtering the Order of Operations

  • Correct Execution of the Project
  • Scope Control
  • WBS Creation
  • Requirements Collection

  • Monitoring the Risks
  • Performing Risk Analysis
  • Planning the Risk Responses
  • Identifying the Risks
  • Implementing the Risk Responses

  • Creating a Project Team
  • Project Governance
  • Influence of an Organization on Project Management
  • Project Life Cycle
  • Project Stakeholders
  • Importance of the Project Team

  • Project Scope Management
  • Project Cost Management
  • Project Resource Management
  • Project Risk Management
  • Project Stakeholder Management
  • Project Integration Management
  • Project Schedule Management
  • Project Quality Management
  • Project Procurement Management

  • Introduction to SAS Software
  • Industries Using SAS
  • Components of SAS System
  • Architecture of SAS System
  • Functionality of SAS System
  • Introduction to SAS Windows

  • Module 1: Working in the SAS Environment
  • Module 2: Creation of Database from Raw Data
  • Module 3: Output Delivery System
  • Module 4: Combining Datasets
  • Module 5: Functions
  • Module 6: Loops in SAS: Do Loops
  • Module 7: Array
  • Module 8: Procedures
  • Module 9: Graphs
  • Module 10: Proc SQL
  • Module 11: Macro

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    FAQ

    Frequently Asked Questions

    Q: What core Python libraries are essential for data analytics? A: Pandas, NumPy, Matplotlib, and Seaborn form the foundational toolkit for Python data analytics. Q: How does Python enable data-driven decision-making? A: Python processes large datasets, extracts insights, and visualizes trends to inform strategic choices. Q: Can beginners master data visualization with Python quickly? A: Yes, interactive plots can be built in under an hour using libraries like Plotly and Altair.

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