Professional Data Analyst Certification (FastTrack) – Zero to Hero in Excel, SQL, Power BI, Tableau & Python
About Course
🔍 Unlock the Power of Data with Analytix Camp‘s Professional Data Analyst Certification Program, a comprehensive course designed to take you from a beginner to an expert in the field of data analysis. Whether you’re just starting out or looking to upgrade your skills, this Data Analyst Training program offers a complete learning path across five of the most essential tools in the industry: Excel, SQL, Power BI, Tableau, and Python. With a focus on real-world applications and practical skills, you’ll be well-equipped to tackle the challenges of today’s data-driven world.
Course Overview:
1. Excel: The Foundation of Data Analysis 📊
Begin your journey with Excel, the bedrock of data analysis used by professionals across the globe. In this segment, you’ll master the art of data management, learning how to efficiently organize and manipulate data to extract valuable insights. We’ll dive deep into Advanced Functions, Formulas, Pivot Tables, Power Query, and Power Pivot, all while ensuring you gain hands-on experience through practical exercises and case studies.
While Excel is a powerful tool, our course is designed with a focus on the most relevant and frequently used features, so we won’t be covering VBA (Visual Basic for Applications). Instead, our emphasis is on equipping you with the practical skills that every data analyst needs, such as data cleaning, sorting, filtering, and advanced charting techniques. By the end of this module, you’ll have a solid foundation in Excel, ready to take on more complex analytical tasks.
2. SQL: Mastering Data Querying and Analysis 🛠️
The next step in your journey is SQL (Structured Query Language), the language of databases. SQL is an indispensable tool for any data analyst, allowing you to query, manipulate, and analyze data stored in relational databases. In this course, you’ll learn how to write efficient SQL queries that retrieve and analyze data, helping you make informed decisions based on solid data foundations.
Our SQL training is focused on practical applications, so while we won’t cover database creation, we will dive deep into the art of querying. You’ll learn how to use SELECT statements, perform complex joins, filter and aggregate data, and utilize subqueries to answer complex business questions. Whether you’re working with MySQL, PostgreSQL, or SQL Server, you’ll develop a versatile skill set that’s applicable across different database systems. This module also includes real-world projects where you’ll apply your skills to analyze large datasets, making you a proficient SQL user ready for the demands of the industry.
3. Power BI: Bringing Data to Life with Dashboards 📈
In the age of data-driven decision-making, being able to visualize data effectively is crucial. That’s where Power BI comes in. In this part of the course, you’ll learn how to transform raw data into compelling visualizations that tell a story. Power BI is a powerful business intelligence tool that allows you to connect to various data sources, perform data modeling, and create interactive dashboards that provide deep insights.
We’ll start with the basics, teaching you how to navigate the Power BI interface, load and clean data, and create simple visualizations. As you progress, you’ll delve into more advanced topics such as data modeling with DAX (Data Analysis Expressions), creating complex measures, and designing professional-grade dashboards. You’ll also learn best practices for dashboard design, ensuring your reports are not only functional but also visually appealing. By the end of this module, you’ll be able to create dynamic dashboards that can be shared across your organization, empowering stakeholders to make data-driven decisions.
4. Tableau: Advanced Data Visualization Techniques 🖼️
Take your data visualization skills to the next level with Tableau, one of the most popular and powerful tools for creating interactive and shareable visualizations. Tableau allows you to explore data in depth and communicate your findings through visually stunning dashboards and reports. This part of the course is designed to give you a comprehensive understanding of Tableau’s capabilities, from the basics to advanced techniques.
You’ll start by learning how to connect to various data sources, create basic visualizations like bar charts, line graphs, and maps, and combine them into interactive dashboards. As you advance, you’ll explore more complex visualizations and techniques, such as using calculated fields, parameters, and filters to make your data stories more dynamic and insightful. Additionally, we’ll cover advanced dashboard design principles and best practices to ensure your work stands out. Whether you’re working with financial data, marketing metrics, or operational performance, Tableau will help you present your data in a way that drives action.
5. Python: Data Analysis with Pandas 🐍
Finally, we delve into Python, a powerful and versatile programming language that has become a cornerstone of data analysis. In this module, you’ll focus on Pandas, the go-to library for data manipulation and analysis in Python. Pandas are particularly powerful when it comes to handling large datasets, and it offers a wide range of functionalities that make data analysis more efficient and effective.
Starting from the basics, you’ll learn how to import, clean, and manipulate data using Pandas. We’ll cover essential techniques such as handling missing data, merging datasets, and performing group-by operations. You’ll also explore more advanced topics like time series analysis, data aggregation, and applying functions across data frames. By the end of this module, you’ll be proficient in using Python for data analysis, able to automate repetitive tasks and perform complex analyses that would be challenging in Excel alone.
Why Enroll? 🎓
This Professional Data Analyst Certification Program is designed to provide you with a comprehensive skill set that’s highly sought after in today’s data-driven job market. By the end of this course, you’ll have mastered five of the most important tools in data analysis—Excel, SQL, Power BI, Tableau, and Python—each of which is a critical component in the toolkit of a successful data professional.
Our course is built around practical, hands-on learning, ensuring that you don’t just learn theory but also how to apply these skills in real-world scenarios. From day one, you’ll be working on projects that mirror the challenges faced by data analysts in the field, giving you the experience and confidence to tackle similar tasks in your career. Whether you’re analyzing sales data, creating dashboards, or automating data workflows, this program will equip you with the tools you need to succeed.
Join us in the Professional Data Analyst Certification Program and take your data skills from zero to hero. This course will not only help you build a strong foundation in data analysis but also give you the expertise to advance your career and make data-driven decisions with confidence. 🚀
Course Content
Module 01 – Microsoft Power BI 🔽
Introducing Power BI Desktop
-
How Learning Power BI Can Help You in Your Career?
-
Downloading: Power BI Desktop
-
Adjusting Options and Settings
-
Power BI Desktop Interface & Workflow
Basics of Power Query
-
Power BI Front-End vs. Back-End
-
Types of Data Connectors (Files, Databases, Folders)
-
The Power Query Editor
-
Basic Table Transformations
-
Data QA & Profiling Tools
-
Text Tools
-
Numerical Tools
-
Date & Time Tools
-
Change Type with Locale
-
Conditional Columns
-
Calculated Column Best Practices
-
Grouping & Aggregating
-
Merging Queries
-
Appending Queries
-
Appending Files from a Folder (Connecting to Folder)
-
Data Source Settings
-
Data Source Parameters
-
Refreshing Queries
Data Modelling
-
Database Normalization
-
Explaining Data Normalization (Need) in Excel
-
Expanded Tables
-
Context Transition
-
Evaluation Order
-
Fact & Dimension Tables
-
Primary & Foreign Keys
-
Relationships vs. Merged Tables
-
Creating Table Relationships
-
Managing & Editing Relationships
-
Star & Snowflake Schemas
-
Active & Inactive Relationships
-
Relationship Cardinality
-
Connecting Multiple Fact Tables
-
Hiding Fields from Report View
-
Data Formats & Categories
-
Creating Hierarchies
Calculated Columns with DAX
-
Intro to DAX Calculated Columns
-
Common Text Functions
-
Basic Date & Time Functions
-
Conditional & Logical Functions
-
The SWITCH Function
DAX Measures
-
Intro to DAX Measures
-
Implicit vs. Explicit Measures
-
Dedicated Measure Tables
-
Understanding Filter Context and Filter Flow
-
Step-by-Step DAX Measure Calculation
-
Common DAX Function Categories
-
Basic Math & Stats Functions
-
Counting Functions
-
Joining Data with RELATED
-
The CALCULATE Function
-
DAX Measure Totals
-
The ALL Function
-
The FILTER Function
-
Iterator (X) Functions
-
Time Intelligence Functions
-
Variables
Data Visualization and Dashboard
-
The 3 Key Questions
-
Sketching the Dashboard Layout
-
Adding Report Pages & Objects
-
Cards & Multi-Row Cards
-
Building & Formatting Charts
-
Line Charts
-
Trend Lines & Forecasts
-
KPI Cards
-
Bar & Donut Charts
-
Basic Filtering Options
-
Table & Matrix Visuals
-
Conditional formatting
-
Top N Filtering
-
Top N Text Cards
-
Map Visuals
-
Report Slicers
-
Gauge Charts
-
Advanced Conditional Formatting
-
Area Charts
-
Drill Up & Drill Down
-
Drillthrough Filters
-
Editing Report Interactions
-
Adding Bookmarks
-
Custom Navigation Buttons
-
Slicer Panels
-
Numeric Range Parameters
-
Fields Parameters
-
Custom Tool Tips
Artificial Intelligence AI Visuals
-
Anomaly Detection
-
Smart Narratives
-
Q&A Visuals
-
Decomposition Trees
-
Key Influencers
Project 02 – Power BI: Maven Market
Project 03 – Power BI: Oodles of Noodles
Project 04 – Power BI: Market Mindz
Project 05 – Power BI: Maven Market (Redesigned)
Module 02 – SQL – Data Exploration and Analysis 🔽
Welcome in the World of SQL
-
Course Outline
-
Discuss the Career Paths
-
What is a Database and it’s need?
-
Why learning SQL?
-
Explaining DataLake, DataWarehouse and DataMart.
-
SQL Server vs MySQL vs PostgreSQL
Basic SQL Queries
-
Installation and Introduction to Azure Data Studio
-
Creating a Connection to a Database
-
Company Profile: Adventure Works?
-
SELECT and FROM
-
SELECT Specific Columns
-
Creating a Column Alias
-
Using WHERE Statement to Filter Rows
-
Checking the Impact of a WHERE Filter
-
Using GROUP BY Statement to Combine Rows
-
Limiting Results to 1 Row for Testing
-
Using GROUP BY to Combine Rows
-
Using HAVING Statement to Filter Grouped Rows
-
Filtering Grouped Rows with HAVING
-
SQL Order of Operations
-
Using ORDER BY Statement to Sort Query Rows
-
Filtering Rows using TOP N
-
Filtering Rows using TOP N Percent
-
Filtering Rows using OFFSET FETCH
-
Filtering Rows DISTINCT Values
Manipulating Values with Functions
-
Counting Rows with COUNT( ) Aggregation
-
How Aggregate Functions Respond to NULL Values
-
The Importance of Data Types
-
Numeric Data Types
-
Numeric Functions
-
Where is the Boolean Data Type
-
Date and Time Data Types
-
Date Parts
-
Date and Time Functions
-
The DATEADD Function
-
Working with Specific Dates
-
Text or String Data Types
-
String Functions
-
Comparison Operators
-
Comparison Operators – Dealing with NULL
-
Logical Operators
-
Logical Operators – Common Errors
-
Advanced Logical Operators – IN and BETWEEN
-
Advanced Logical Operators – LIKE
-
Using IIF Statements to Create a Conditional Column
-
Using a CASE Statement for Multiple Conditions
-
Basic SQL Formatting
-
Using IIF in a WHERE Statement
-
Replacing NULL Using IIF and ISNULL
-
Using CAST to Change the Data Type
SQL Theory
-
Fact and Dimension Tables
-
Relationships & Keys
-
The Star Schema
-
Snowflake Hybrid Schema
Working with Multiple Tables
-
Relationships and ER Diagrams
-
Purpose of DW Relationships
-
Internet Sales Schema
-
Types of JOIN
-
A Basic INNER JOIN Using Sales and Customers
-
Returning Only the TOP 100 Customers
-
INNER JOIN the Another Table
-
HAVING or WHERE
-
When INNER JOIN Doesn’t Work
-
Is INNER and LEFT Join are same in Industries?
-
RIGHT JOIN Application
-
LEFT JOIN vs RIGHT JOIN
-
Self Joins
-
Cross Joins
-
Natural Join
-
Appending Data with a UNION
-
Exploring the Reseller Schema
-
Creating a UNION between Fact Tables
-
Identifying the Source of Each UNION Row
-
Using ORDER BY with a UNION
-
Creating a View
-
Querying a View
-
Creating Dynamic Results Using Subqueries
-
Explain Scalar, Multi Rows and Correlation Sub Queries
-
Examples: Sub Queries
Windows Functions
-
Window Functions Introduction
-
Window Functions Basics – Over + Partition By
-
Row Number
-
Customers Largest Purchases
-
Rank and Dense Rank
-
Art Ranking
-
Lag and Lead
Project 06: World Life Expectancy
Project 07: US Household Income
Project 08: Power BI Visualizations + SQL Data Analysis
Module 03: Microsoft Excel – Reporting, Analysis and Dashboard
Excel Basics and Functions
-
Introduction of MS Excel
-
Text Functions
-
Aggregation Functions
-
Conditional Functions
-
Lookup Functions
-
Data Cleaning
Data Visualization
-
Columns, Bar, Line Charts
-
Pie, Donut and Histogram Chart
-
Waterfall Chart
-
Combo Charts
-
Advanced Charting Techniques
Scenario and Sensitivity Analysis
-
Data Tables
-
Goal Seek
-
Scenario Manager
-
Solver
Project 09 – Interactive Dashboard with Excel Functions
Getting Started with Pivot Tables
-
Why Pivot Tables?
-
Structuring Source Data for Analysis in Excel
-
Creating Your First Pivot Table
-
Navigating the Pivot Table Field List
-
Exploring Pivot Table Analyze & Design Options
-
Selecting, Clearing, Moving & Copying Pivot Tables
-
Refreshing & Updating Pivot Tables
-
Dealing with Growing Source Data
-
How Excel Pivot Tables ACTUALLY Work
Formatting Data with Pivot Tables
-
Changing Pivot Table Number Formats
-
Formatting Empty Pivot Table Cells
-
Configuring Pivot Table Layouts & Styles
-
Using Tabular Pivot Tables to Create New Source Data
-
Customizing Pivot Table Headers & Labels
-
Applying Basic Conditional Formats to Pivot Tables
-
Adding Data Bars with Invisible Text
-
Applying Advanced Conditional Formats to Pivot Tables
Sorting, Filtering & Grouping Data with Pivot Tables
-
Sorting Data with Pivot Tables
-
Fixing Incorrect Alphabetical Sorting
-
Filtering Data with Pivot Table Label & Selection Filters
-
Pivot Table Label Filters with Wildcards
-
Filtering Data with Pivot Table Value Filters
-
Enabling Multiple Pivot Table Filters
-
Grouping & Segmenting Data with Pivot Tables
-
Enabling & Disabling Automatic Date Grouping
-
Filtering Data with Pivot Table Slicers & Timelines
-
Breaking Out Pivot Table Report Filter Pages
Enriching Data with Pivot Table Calculated Values & Fields
-
Aggregating & Summarizing Data with Pivot Tables
-
Defining Value Calculations with Pivot Tables
-
Calculating Pivot Table Values: % of Column/Row
-
Calculating Pivot Table Values: % of Parent
-
Calculating Pivot Table Values: Difference From
-
Calculating Pivot Table Values: Running Total
-
Calculating Pivot Table Values: Rank
-
Defining Calculated Fields with Pivot Tables
-
Creating Calculated Fields in Pivot Tables vs. Source Data
-
Pivot Table Calculations Using Count Columns
Project 10 – Dynamic Dashboard using Pivot Tables
Data Transformation with Power Query
-
Introduction of Power Query in Excel
-
Connecting to the Database
-
Cleaning Data In Power Query
-
Merging Data in Power Query
-
Adding New Columns in Power Query
-
Loading Power Query Data to Excel
Creating Reports using Pivot Table and Power Pivot.
-
Pivot Table For Movie Analytics
-
Creating a Report Using Pivot Table
-
Using Power Pivot & DAX For Powerful Business Reports
-
Adding Targets Using Data Modelling in Power Pivot
-
Breaking Down Complex Problems: Thinking Process of a Highly Paid Data Analyst
-
More Business Metrics and Conditional Formatting
-
VBA Basics & How Much VBA You Should Learn
Project 11 – 365 Hardware’s Company
-
365 Hardware’s Business Model
-
Usage of Excel & Business Requirement in 365 Hardware
-
ETL (Extract, Transform and Load Data) in Excel
-
Business Report: Solution Design Thought Process
-
Creating Connections Among Tables Using Data Modelling
-
Adding a Date Table Using Power Query
-
Functional Knowledge: Sales
-
Sales Analytics: Creating Customer Performance Report
-
User Empathetic Report Design
-
Sales Analytics: Creating Market Performance vs Targets Reported
-
Understanding P & L
-
Functional Knowledge: Finance
-
Adding the Finance Data to Data Model
-
Finance Analytics: P & L by Year Report
-
Fine Tuning P & L By Year Report
-
Adding Months & Quarters in Data Model
-
Finance Analytics: P & L by Months Report
-
Fine Tuning P & L By Month Report
-
Wanda’s Challenge With Prioritizing Projects
-
Peter & Tony Creates a Project Priority Matrix
-
Bruce Haryali Needs Help With Excel
-
Peter & Tony Creates a Scenario Planning Tool
(ADD ON): Module 04 – Tableau – Dashboard Reporting and Visualization 🔽
Introduction of Tableau
-
What is Tableau?
-
Installing Tableau
-
Tableau UI Walkthrough
Building your First Visualization
-
Creating a Bar Chart
-
Tableau Colors
-
Filters and Formatting
Calculations, Groups, Bins and Parameter
-
Time Series Aggregations
-
Categorical Aggregations
-
Calculated Fields
-
Table Calculations
-
Groups
-
Bins and Parameters
Creating Dashboard + Mini Project
-
Creating our Visualizations
-
Building the Dashboard
-
Formatting our Dashboard
-
Adding Filters
-
Adding Highlighting
-
Sharing our Dashboard
Joins, Relationships and Union
-
What are Joins?
-
Connecting Multiple Datasets
-
Unions
-
Joining on Real Data
Storytelling, Maps and Hierarchy
-
Creating Hierarchies
-
Building Geographical Maps
-
Creating Visualizations for Stories
-
Creating our Story (Mini Project)
Visualizations in Tableau
-
Histograms
-
Box and Whisker Plots
-
Area Charts
-
Dual Combination Chart
-
Gannt Chart
-
TreeMaps and Packed Bubble Charts
Advanced Data Preparation and Cleaning
-
Data Interpreter – Part 1
-
Data Interpreter – Part 2 + Pivoting Data
-
Splitting and Combining Columns
-
Analytics Tab
-
Clusters
-
Forecasting
Project 12: Tableau KPI Dashboard
Module 05 – Python – Introduction and Setup 🔽
-
Course Overview
-
Python Overview
-
Anaconda Distribution Installation
-
Jupyter Notebook 101
-
Jupyter Notebook – Adding Comments in Cells
-
Course Resources – Important!
Objects, Variables and Data Types
-
Objects and Variables Overview
-
Numbers
-
Strings
-
String Operations
-
String Methods and Properties
-
String Concatenation and Formatting
-
Lists
-
Dictionaries
-
Tuples and Sets
-
Booleans
Control Flow and Loops
-
Python Operators
-
Control Flow
-
For Loops
-
For Loops (continued)
-
While Loops
-
Break, Continue and Pass Statements
-
List Comprehension
-
IN and NOT IN
Functions
-
Built-In Functions
-
User Defined Functions
-
User Defined Functions – Examples
-
Arguments and Keyword Arguments
-
Map and Filter
-
Lambda Functions
-
Errors and Exception Handling
Challenge Section – Core Python
-
Challenge Questions Overview
-
Solutions Walkthrough
Modules, Packages and Libraries
-
Built-In Modules
-
External Libraries
NumPy (Numerical Array Operations)
-
NumPy Overview
-
Array Slicing and Indexing
-
Array Manipulation Functions
-
Additional Array Creation Functions
-
Array Arithmetic and Mathematical Functions
-
IO Functions in NumPy
Challenge Section – NumPy
-
Challenge Questions
-
Challenge Solutions
Pandas (Data Analysis and Manipulation)
-
Pandas Overview
-
Introduction to Series
-
Introduction to DataFrames
-
Selecting Data
-
Selecting Data 2
-
Data Manipulation 1
-
Data Manipulation 2
-
Data Aggregation and Grouping
-
Data Cleansing
-
Combining DataFrames
-
Windowing Operations
Challenge Section – Pandas
-
Challenge Questions – TFL Dataset
-
Solutions Walkthrough
-
Challenge Questions – Employees Dataset
-
Solutions Walkthrough
Connecting to Data Sources
-
Excel and CSV
-
HTML
-
Databases
-
Pandas Input and Output Methods
Matplotlib (Data Vizualization)
-
Matplotlib Overview
-
Choosing the Right Chart Type
-
Creating a Plot Area 1
-
Creating a Plot Area 2
-
Bar Plots
-
Line Plots
-
Scatter Plots
-
Histograms
-
Box Plots and Violin Plots
-
Style and Presentation
-
Additional Resources and Cheat Sheets
Challenge Section – Matplotlib
-
Challenge Questions Overview
-
Solutions Walkthrough
Seaborn (Statistical Data Visualization)
-
Seaborn Overview
-
Categorical Plots
-
Relational Plots
-
Distribution Plots
-
Regression Plots
-
Matrix Plots
-
Multi Plot Grids
-
Style and Presentation
Challenge Section – Seaborn
-
Challenge Questions Overview
-
Solutions Walkthrough