DATA ANALYTICS
DATA ANALYTICS COURSE
Course Introduction:
Welcome to our comprehensive Data Analytics course! This
program is designed to equip you with the essential skills and knowledge needed
to excel in the dynamic field of data analytics. Throughout this course, you
will gain proficiency in Excel, SQL, Python, Power BI, and other crucial tools,
enabling you to effectively collect, clean, analyze, and visualize data.
Real-world projects and career guidance are integrated to provide practical
experience and guide you towards a successful career in data analytics.
Job Roles in Data Analytics:
Upon completing this course, you'll be prepared for a diverse
range of job roles in the data analytics domain, including:
·
Data Analysts
·
Business Intelligence Analysts
·
SQL Developers
·
Business Analysts
·
Power BI Analysts
·
Python Developers
These roles span from data cleaning and visualization to
advanced analytics, database management, predictive modeling, Power BI
analysis, and Python development, offering a wide array of career opportunities
in the data-driven industry.
Syllabus
Module 1: Excel
- Basic
functions (SUM, AVERAGE, MAX, MIN)
- Advanced
functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
- Pivot
tables and charts
- Data
validation and conditional formatting
- Macros
and automation
- Data
cleaning and manipulation
- Charts
in Excel:
- Creating
various types of charts (bar, line, pie, etc.)
- Formatting
and customizing charts
- Adding
trendlines and annotations to charts
- Creating
combination charts and dual-axis charts
Module 2: SQL
· Introduction to SQL
· Working with SQL Databases
· Understanding databases and their structure
· Installing and setting up SQL environments (e.g., MySQL, MS SQL Server)
· Basic SQL Operations
· Creating databases and tables
· Inserting, updating, and deleting data
· Retrieving data using SELECT statements
· Filtering and Sorting Data
· Using WHERE clause for data filtering
· Comparison operators
· Logical operators
· Sorting retrieved data using ORDER BY
· Aggregate Functions and Grouping
· Understanding aggregate functions (COUNT, SUM, AVG, MIN, MAX)
· Performing calculations on grouped data
· Using GROUP BY clause to aggregate data
· Working with Strings and Dates
· Manipulating strings with functions like CONCAT, SUBSTRING
· Handling date and time data using date functions
· Joining Tables
· Understanding the concept of table joins (Ven Diagrams)
· Implementing INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN
· Handling NULL values in joins
· Subqueries and Derived Tables
· Understanding subqueries and their role in SQL
· Using subqueries for complex queries and aggregations
· Utilizing derived tables for intermediate results
Module 3: MS Power BI
· Connecting to different data sources (Excel, SQL Server, SharePoint, Salesforce, etc.)
· Importing data from various file formats (CSV, Excel, JSON, etc.)
· Connecting to databases, cloud services, and online platforms
· Data cleansing and transformation techniques
· Merging and appending data from different sources
· Creating custom columns using M language
· Understanding and using data types
· Measures and Calculated Columns (DAX):
· Introduction to DAX (Data Analysis Expressions)
· Creating measures for calculations (e.g., SUM, AVERAGE, etc.)
· Writing DAX expressions for calculated columns
· Time intelligence functions (e.g., YTD, MTD, QTD)
· Relationships between tables (one-to-many, many-to-many)
· Creating hierarchies for improved data visualization
· Understanding and managing cardinality
· Creating calculated tables and relationships
· Building different types of visualizations (charts, graphs, maps, etc.)
· Formatting and customizing visuals for better presentation
· Utilizing slicers and filters for interactivity
· Drill-down and drill-through capabilities
· Publishing reports to the Power BI service
· Setting up dashboards for sharing and collaboration
· Managing workspaces and permissions
· Scheduling data refreshes
· Sharing and publishing reports
· Advanced Power BI features and integration with other tools
Module 4: PythonProgramming language
· Introduction to Python
· Role of python in Data science
· Explanation of Python's simplicity and readability.
· Python Syntax
· Data Types : (integers, floats, strings, booleans).
· Variablesdeclaration
· Variables assignment
· Variables naming conventions
· Lists in Python
· List creation, indexing, slicing, and modifying
· Sets in Python:
· Set operations (union, intersection, difference).
· Sets: data deduplication
· Sets: unique value extraction.
· List Comprehensions:
· Control Structures:
o for loops,
o while loops.
· Conditional Statements (if and elif)
· Logical operators (and, or, not)
· Lambda Functions
· Map, Filter
· Create, Read, Write Files
· File Operations & Errors
· Introduction to Classes and Objects (OOPS)
· Classes & Objects
· Create Class & Methods
· Working with Objects
· The init() Method
· Modify Properties & Methods
· 'self' Parameter
· Delete Objects
· ‘Continue’ and 'pass' Statements
Module 5: Pythonfor Data Analytics and Data Science
· Real-world data science scenarios using Python.
· Numpy
· Pandas
· Matplotlib
· Types of Data: Structured, Unstructured, Semi-Structured:
· Structured (tabular)
· Unstructured (text, images), and
· Semi-structured (XML, JSON).
· Numpy
· Introduction to Array
· Creation and Printing of an array
· Basic Operations in NumPy
· Indexing
· Numpy: Where, count, arg
· Pandas
· What is Pandas Data frame
· Tabular data structure with rows and columns
· Series, Index
· Read_csv, Head, Tail
· Shape, Columns
· Iloc, loc, Drop
· GroupBy: Grouping and aggregation operations.
· Reshaping: Dataframe manipulation
· Plotting: Data visualization tools.
· Missing Data:
· Merge and Join: Combining DataFrames.
· Matplotlib
· Figure: Top-level container.
· Axes: Individual plots.
· Line Plot: Connects data points.
· Scatter Plot: Displays individual points.
· Bar Plot: Uses rectangular bars.
· Histogram: Shows data distribution.
· Pie Chart: Displays composition.
· Annotations: Adds text/arrows.
· Subplots: Divides figure.
· Styles: Customizes appearance.
· Case Study on Exploratory Data Analysis (EDA) and Visualizations
· What is EDA?
· Uni – Variate Analysis
· Bi-Variate Analysis
· More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.
Module 6: Stats and Maths
·
Statistics in Data science:
o What is Statistics?
o Role in Data Science
o Population vs. Sample
o Parameter vs. Statistic
o Types of Variables
· Data Gathering Techniques
o Collecting Data
o Sampling Techniques:
o Convenience, Simple Random Sampling
o Stratified, Systematic, Cluster Sampling
Module 7: Real-world Projects
- Students
work on 2-3 real-world data analytics projects
- Customer
Churn Analysis
- Product
Sales Data Analysis
- Financial
Performance Analysis
- Movie
Sales Visualization
- Emphasis
on applying skills learned in previous modules
- Data
collection, cleaning, analysis, and visualization
- Report
generation and presentation skills
Module 8: Career Guidance
- Resume
building and job search strategies
- Interview
preparation and mock interviews
- Networking
and professional development
- Industry
trends and emerging technologies in data analytics
- Internship
and job placement assistance
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