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WELCOME TO NICE IT SERVICES

BROCHURE

WELCOME TO NICE IT SERVICES

BROCHURE

Data Analysis Through Python

Join the program and get the opportunity to learn under the guidance of a Data Analysis specialist.

4.8 out of 5 based on 50 reviews

Data Analysis Through Python

The Data Analysis Through Python course focuses on leveraging Python for efficient data analysis and visualization. It begins with Python basics, including data types, functions, and libraries. The curriculum covers key data analysis libraries such as NumPy for numerical computations, Pandas for data manipulation, and Matplotlib/Seaborn for creating insightful visualizations. Students learn to clean, transform, and analyze datasets, perform statistical operations, and interpret results.

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Total 6 Modules ( Click For Details )

MODULE 1 : PYTHON PROGRAMMING
1. Introduction
  • History
  • Features Setting up path
  • Working with Python Basic Syntax
  • Variable & Data Types
  • Operators (Arithmetic, Relational,Membership, Bitwise etc.)
  • Punctuators, Indentation, Comments
2. Conditional Statements
  • If
  • If- else
  • If-elif
  • Nested if-else
3. Iterative Statements (Looping)
  • For
  • While
  • Loop else statement
  • Nested loops
4. Control Statements
  • Break
  • Continue
  • Pass
5. String Manipulation
  • Accessing Strings
  • Basic Operations
  • String slices
  • Function and Methods
6. Lists
  • Introduction
  • Creating List
  • Accessing list
  • List Operations
  • Function and Methods
  • Working with lists (List Programs)
7. Tuple
  • Introduction.
  • Accessing Tuples.
  • Operations.
  • Working.
  • Functions and Methods.
8. Dictionaries
  • Introduction (Key: Values)
  • Accessing values/elements
  • Dictionaries Properties
  • Functions and Methods
9. Sorting & Searching Concepts
  • What is Sorting
  • Bubble Sort
  • Insertion Sort
  • Binary Search
10. Functions
  • Defining a function
  • Calling a function
  • Types of functions
  • Function Arguments
  • Scope of Variables
    • Global and local variables
  • Returning Values from function
11. Python Libraries/ Packages/ Modules
  • Python standard libraries
  • Structure of a module
  • Importing module
  • Math module
  • Random module
  • Urlib and WebBrowser modules
  • Packages
  • Creating a Python Library/package(s)
  • Importing Python Libraries
12. Input-Output (File Handling)
  • Data Files (Text and Binary Files)
  • Opening/Closing a File
  • Reading data from File
  • Writing data in a file from keyboard
  • Printing on screen
  • File Handling Functions
  • Standard Input, Output and Error streams
13. Exceptional Handling
  • Exception
  • Exception Handling Except clause
  • Try ? Finally clause
14. Regular Expression
  • Pattern Matching
  • Meta Characters for making patterns
  • match(), sub(), findall(), search(), split()method
MODULE 2 : ADVANCED EXCEL
1. Basic Excel
  • Workbook, Worksheet, Workspaces
  • Row & Column Settings
  • Calculation Basics in Excel
  • References (Relative, Absolute, Mixed)
  • Find & Replacing Data
  • Header & Footer setting
  • Working with Chart
  • Functions (Financial, Statistical etc.)
  • Formulas (DSUM, DMAX, DMIN, DCOUNT)
  • If And Analysis, What if Analysis
  • Goal Seek, Solver, Scenarios
  • Naming Cell Range
  • Protecting sheet & workbook Autofill, Autocorrect, Autoformat

 

2. Data Analysis In Excel (Advance Excel)
  • Importing and Exporting Data
  • Auditing, Freeze Panes
  • Grouping and Subtotal
  • Macro (recording tool)/EDITING
  • Customizing Sheet/Cells /Rows/Columns
  • Customizing Excel Window
  • Sorting and Filtering Data, Advance Filter
  • Data Validation
  • Pivot Table, Pivot Chart
  • Consolidation of Sheets
  • H Lookup, V Lookup, X Lookup
  • Creating Reports, Marks sheet etc
  • Loan Sheet Preparation (PPMT, IPMT, PMT)
  • RD CALCULATION
  • Printing Worksheet, Custom List
  • Working with Excel CSV files
  • Making Excel to PDF file
MODULE 3 : DATABASE (MYSQL)
  • DATABASE MGMT. SYSTEM (DBMS)
  • DBMS TERMINOLOGIES
  • RELATIONAL DATABASE
  • Introduction to SQL/MySQL
  • SQL DATA TYPES
  • INTEGRITY CONSTRAINTS
  • CREATING TABLES BASED ON INTEGRITY CONSTRAINTS
  • ALTERING TABLE
    • ADDING NEW FIELDS
    • CHANGING EXISTING FIELDS
  • INSERT, UPDATE, DELETE
  • SELECT COMMAND
  • WHERE (RETRIEVAL OF SPECIFIC ROWS)
  • WORKING WITH EXPRESSIONS
  • CHECKING MULTIPLE CONDITIONS (AND, OR, NOT OPERATOR)
  • EXISTS, NOT EXISTS
  • BETWEEN, NOT BETWEEN

ARRANGING RECORD (ORDER

  • DISTINCT CLAUSE
  • WORKING WITH NULL VALUES
  • SQL FUNCTIONS
    • AGGREGATE FUNCTIONS
    • ARITHMETIC FUNCTIONS
    • CHARACTER/STRING FUNCTIONS
    • DATE FUNCTIONS
  • GROUPING RESULTS (GROUP BY)
  • PLACING CONDITIONS ON GROUPS (HAVING CLAUSE)
  • TABLE ALIASES
  • JOINS
    • MULTI-TABLE JOINS
    • EQUI JOIN
    • CARTESIAN JOIN
    • OUTER JOINS
    • UNIONS IN SQL
  • CUSTOMISING TABLE
  • CREATING TABLE FROM OTHER TABLE
  • CREATING DUPLICATE TABLE
  • RENAMING A TABLE
  • INDEXES IN SQL
  • TRIGGER
MODULE 4 : DATA ANALYTICS WITH PYTHON
1. Getting Started With Python Libraries
  • What is data analysis?
  • Why python for data analysis?
  • Essential Python Libraries
  • Installation on and setup
  • Ipython
  • Jupyter Notebook
2. Python Arrays (NumPy)
  • Pandas Series
  • Pandas Dataframes (2-Dimensional)
  • Data aggregation with pandas
  • Data Indexing and selection
  • Operation on Data in Pandas
  • loc and iloc map
  • apply,apply_map, group_by
  • Querying data in pandas
  • Dealing with dates
  • Reading and Writing to CSV fi les with pandas
  • Reading and Writing to SQL with pandas
  • Reading and Writing to HTML files with panda
# Data Visualization
3. Python Matplotlib
  • Matplotlib Pyplot
  • Matplotlib Plotting
  • Matplotlib Markers
  • Matplotlib Line
  • Matplotlib Scatter
  • Matplotlib Bars
  • Matplotlib Pie Charts
4. Introduction oF Seaborn
  • Categorical Plots, Bar Plots, Box Plots
  • Heatmaps Plots, Pair Plots
  • Regression Plots
  • Style and Color
5. Plotly – Python Plotting
  •  Introduc on to Plotly – Python Plotting, Plotly
6. Geographical Plotting
  • Introduction to Geographical plotting
  • Choropleth Maps
7. Database Connectivity With MySQL
  • MySQL Operations
  • Database Connection
  • Creating New Database
  • Creating Tables
  • Inserting Records in Table
  • Fetching Records from Database Using Python
  • Read Operation using Select, Where,
  • OrderBy etc
  • Update Operations
  • Join Operations
  • Performing Transactions

 

MODULE 5 : STATISTICS, PROBABILITY & BUSINESS ANALYTICS
1. Overview oF Statics
  • Data types and their measures
  • Arithmetic Mean, Harmonic Mean, Geometric Mean
  • Meadian, Mode, Variance, Standard Deviation
  • Quartile: First quartile, Second quartile, Third quartile, IQR
2. Probability Distribution
  • Introduction of probability, Conditional probability
  • Normal distribution, Uniform distribution , Frequency distribution, Central limit theorem
MODULE 6 : POWER BI
1. Power BI
  • Introduction to Power BI, Uses Of Power Bi, The Flow Of Work In Power Bi,
  • Working With Power Bi, Basic Components Of Power Bi
  • Comparison Of Power Bi Version, Data Model And Importance Of Data Modeling
  • Data Sources: How to connect and import data from different sources (Excel etc)
2. Power BI Desktop and Data Transformation
  • Data Sources in Power BI Desktop, Loading Data in Power BI Desktop, Views in Power BI Desktop
  • Query Editor In Power BI
  • Transform, Clean, Shape, and Model Data
  • Manage Data Relationship, Editing a Relationship
  • Cross Filter Direction, Saving Workfile, Measures
3. Data Analysis Expression (DAX)
  • Introduction and Importance to DAX, Data Types in DAX
  • DAX Calculation Types, Steps to Create Calculated Columns
  • Measures in DAX, DAX Syntax, DAX Functions, DAX Operators, DAX Tables and Filtering
4. Data Visualization
  • Introduction to Visuals In Power BI, Visualization Charts in Power BI
  • Matrixes and Tables, Slicers and Map Visualizations
  • Gauges and Single Number Cards, Modifying Colors in Charts And Visuals
  • Shapes, Text Boxes, and Images, Custom Visuals
  • Page Layout and Formatting, Bookmarks and Selection Pane, KPI Visuals, Z-order, Grouping and Binding
5. Power BI Service
  • Introduction to Power BI Service
  • Creating a Dashboard, Configuring a Dashboard, Quick Insights in Power BI
  • Power BI Q&A, Ask Questions about your Data
  • Power BI Embedded, Bookmarks and buttons
  • Creating Reports and Dashboards: Building reports by combining visualizations into a cohesive layout and creating interactive dashboards for data exploration

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