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Become a
Full-Stack Data Analyst
In 6 Months
Average Full Stack Data Analyst Salary: $92,000
What You Get In This Course:
Full Excel Course
Access to complete Microsoft Excel For Data Analysis course. From beginner level to advanced Excel skills. Become a master at excel and irresistible in your team.
Full SQL For Data Analytics Course
Go from Basic to Advanced SQL for Data Analytics. Everything you need to know to become expert in SQL
Full Data Visualisation Course
Go from Basic Data Visualisation techniques to advanced plotting and graph techniques. Everything you need to know to become expert in Data Visualisation.
Master Tableau For Data Analytics
Get access to complete Tableau for Data Visualisation course. Tableau is one of the most powerful tools for Data Visualisation in the industry and mastering it is a HUGE plus to your career.
Master Microsoft Power BI
Microsoft Power BI is an industry leader in data visualisation. Data Analysts with Power BI skills are 10X better of securing a job offer faster with higher salary than others. Get access to full Power BI course.
Data Storytelling & Presentation Skills
Learn how to present your data to your stake-holders and tell stories about it. Storytelling is a significant part of your skills as a Full Stack Data Analyst. Get to know how these professionals tell stories about their data to make
Master Python For Data Analytics
Learn beginner to advanced Python concepts for data analytics Gain a solid background in Python program. Learn robust Python libraries and frameworks such as Numpy, Pandas, Matpltlib, Seaborn, Plotly, ggplot, SKlearn, etc.
Web Scraping For Data Analytics
Learn how data analysts perform professional web scraping to fetch data from various web portals to solve business problems. Learn robust data analytics web scraping tools and techniques and frameworks like Beautifulsoup, Request, Selenium, and Scrapy.
Hands-On Projects
Gain a practical understanding of the concepts by working on 10+ carefully selected Projects and Assignments.
Course Curriculum
Introduction
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1ST MONTH
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WEEK 1 :: Data Analytics Overview
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- Lecture Slide
- Introduction To Data Analytics (3:11)
- What is Data and Data Analytics (3:43)
- Who is a Data Analyst (1:11)
- Data Analytics Vs Data Science Vs Big Data (4:15)
- What Data Analyst Do (3:51)
- Types of Data Analytics (5:11)
- Tools for a Data Analyst (1:25)
- Practical Applications of Data Analytics (4:13)
- Data Analysis Lifecycle (3:49)
- Typical Backgrounds of Data Analysts (1:41)
- What skills do I need to develop to become a Data Analyst (6:04)
- Career Opportunities in Data Analytics (1:20)
- Typical Salary of a Data Analyst (3:55)
MASTER MICROSOFT EXCEL FOR DATA ANALYSIS
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BEGINNER LEVEL : Introduction To Microsoft Excel
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- EXERCISE FILES
- General Overview (4:58)
- Ribbon Menu (7:41)
- Varied Excel Versions (1:45)
- Using the Quick Access Toolbar and Ribbon Menu (4:50)
- Shortcut menus and the Mini Toolbar (3:52)
- Understanding workbooks and worksheets (4:15)
- Using Excel Help (3:56)
- DATA ENTRY: Exploring data entry, editing, and AutoFill (8:25)
- DATA ENTRY: Working with dates and times (5:49)
- DATA ENTRY: Using Undo and Redo (3:37)
- DATA ENTRY: Using Save or Save As (4:54)
- FORMULAS: Using simple formulas (5:43)
- FORMULAS: Copying a formula into adjacent cells (4:16)
- FORMULAS: Using SUM | AVERAGE | Autosum (6:02)
- FUNCTIONS: XLOOKUP | VLOOKUP | HLOOKUP functions (9:01)
- VLOOKUP
- FORMAT: Exploring font styles and effects (7:27)
- FORMAT : Adjusting row heights and column widths (5:30)
- FORMAT : Rows and columns: Insert, delete, hide, and unhide (10:07)
- FORMAT : Moving, copying, and inserting data (6:45)
- FORMAT : Finding and replacing data (7:28)
- PRINT: Page Layout view and commands (10:49)
- PRINT : Using Page Break Preview and print setup options (3:15)
- VISUALIZATION : Creating charts (5:10)
- Freezing and unfreezing panes (4:36)
- Renaming, inserting, and deleting sheets (6:21)
- Moving, copying, and grouping sheets (7:55)
- Sorting data (6:22)
- Using filters (7:10)
- Creating PivotTables (14:33)
- New data types coming to Excel for 365 (7:25)
- Protecting worksheets and workbooks (5:19)
- Sharing workbooks (2:26)
WEEK 2 : INTERMEDIATE EXCEL
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Data Preparation In Excel
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Sorting Data
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Auto Subtotals
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Data Filtering
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EXCEL PRACTICE EXERCISES
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WEEK 3 :: COMPLETE SQL FOR DATA ANALYTICS
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SQL : BEGINNER LEVEL
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- Introduction To SQL for Data Science (6:06)
- Types of Databases (4:20)
- What is a Query? (3:01)
- What is SQL? (3:18)
- SQL or SEQUEL? (2:59)
- SQL Installation (1:36)
- SQL Installation Guide For MacOS (4:53)
- SQL Installation Guide For Windows (4:01)
- Extra Help in Installing SQL (0:46)
- Overview of SQL workbench (13:49)
SQL Commands
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Understanding and Creating SQL Databases
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Understanding and Creating SQL Tables
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Types Of SQL KEYS
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Data Types In SQL
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CREATE Table and INSERT Data into Table
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SQL Constraints
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WEEK 4 :: INTERMEDIATE LEVEL SQL
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- Creating Exiting Databases (6:15)
- Overview Of Existing Databases (5:12)
- The SELECT Statement in Details (9:49)
- The ORDER BY Clause (2:01)
- The WHERE Clause (4:46)
- Operation with SELECT statement (7:10)
- Aliasing in SQL (9:03)
- Exercise 1 and solution (5:28)
- The DISTINCT Keyword (3:28)
- WHERE Clause with SQL Comparison operators (7:15)
- Exercise 2 and Solution (3:17)
- The AND, OR and NOT Operators (11:48)
- Exercise 3 and Solution (5:35)
- The IN Operator (3:02)
- Exercise 4 and Solution (2:18)
- The BETWEEN Operator (2:41)
- Exercise 5 and Solution (3:16)
- The LIKE Operator (7:59)
- Exercise 6 and Solution (3:51)
- The REGEXP Operator (10:31)
- Exercise 7 and Solution (7:21)
- IS NULL & IS NOT NULL Operator (3:12)
- Exercise 8 and Solution (2:44)
- The ORDER BY Clause in Details (2:01)
- The LIMIT Clause (2:31)
- Exercise 9 and Solution (2:53)
SQL JOINS
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- Introduction To SQL JOINS (9:04)
- Exercise 10 and Solution (7:24)
- Joining Across Multiple Databases (6:51)
- Exercise 11 and Solution (8:27)
- Joining Table to Itself (7:56)
- Joining Across Multiple SQL Tables (11:16)
- LEFT and RIGHT JOIN (6:32)
- Exercise 12 and Solution (6:27)
- Exercise 13 and Solution (7:24)
Working With Existing SQL Table
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SQL VIEW
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SQL Data Summarization: Aggregation Functions
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Advance SQL Functions
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SQL : ADVANCED LEVEL
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SQL Stored Procedure
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Triggers
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Transactions
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4TH MONTH
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MASTER PYTHON FOR DATA ANALYSIS
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Introduction To Google Colab
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Datasets
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Hands-On With Python
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- Lecture Resources
- Python Hands-On: Introduction (1:00)
- Hands-On With Python: Keywords And Identifiers (12:53)
- Hands-On Coding- Python Comments (7:09)
- Hands-On Coding- Python Docstring (3:25)
- Hands-On Coding- Python Variables (9:03)
- Hands-On Coding- Rules and Naming Conventions for Python Variables (7:39)
Python Output(), Input() and Import() Functions
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Python Operators
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- Hands-On Coding- Arithmetic Operators (2:12)
- Hands-On Coding- Comparison Operators (1:53)
- Hands-On Coding- Logical Operators (7:38)
- Hands-On Coding- Bitwise Operators (7:51)
- Hands-On Coding- Assignment Operators (3:34)
- Python Hands-On- Special Operators (2:00)
- Hands-On Coding- Membership Operators (2:51)
Python Flow Control
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WEEK 2: Python Functions
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Python Global and Local Variables
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Working With Files In Python
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Python Modules
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Python Packages and Libraries
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Data Types In Python
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Extra Content
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Numpy
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after you enroll
- Lecture Resources
- Introduction To Numpy (11:30)
- Numpy: Creating Multi-Dimensional Arrays (1:53)
- Numpy: Arange Function (5:53)
- Numpy: Zeros, Ones and Eye functions (4:46)
- Numpy: Reshape Function (1:23)
- Numpy: Linspace (2:23)
- Numpy: Resize Function (5:23)
- Numpy:Generating Random Values With random.rand (3:04)
- Numpy:Generating Random Values With random.randn (2:26)
- Numpy:Generating Random Values With random.randint (3:40)
- Numpy: Indexing & Slicing (17:00)
- Numpy: Broadcasting (1:17)
- Numpy: How To Create A Copy Dataset (4:28)
- Numpy- DataFrame Introduction (15:25)
Pandas
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- Pandas Resources
- Pandas- Series 1 (19:21)
- Pandas- Series 2 (11:05)
- Pandas- Loc & iLoc (7:48)
- Pandas- DataFrame Introduction (4:17)
- Pandas- Operations On Pandas DataFrame (9:10)
- Pandas- Selection And Indexing On Pandas DataFrame (3:12)
- Pandas- Reading A Dataset Into Pandas DataFrame (8:32)
- Pandas- Adding A Column To Pandas DataFrame (4:33)
- Pandas- How To Drop Columns And Rows In Pandas DataFrame (11:03)
- Pandas- How To Reset Index In Pandas Dataframe (3:32)
- Pandas- How To Rename A Column In Pandas Dataframe (6:29)
- Pandas- Tail(), Column and Index (2:56)
- Pandas- How To Check For Missing Values or Null Values(isnull() Vs Isna()) (6:16)
- Pandas- Pandas Describe Function (5:40)
- Pandas- Conditional Selection With Pandas (9:14)
- Pandas- How To Deal With Null Values (7:14)
- Pandas- How To Sort Values In Pandas (3:10)
- Pandas- Pandas Groupby (0:37)
- Pandas- Count() & Value_Count() (2:14)
- Pandas- Concatenate Function (6:47)
- Pandas- Join & Merge(Creating Dataset) (3:45)
- Pandas-Join (9:49)
- Pandas- Merge (7:55)
Data Visualisation: MatplotIib And Seaborn
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WEB SCRAPING
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PROJECT: Wikipedia Web Scraping
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Online Book Store Web Scrapping
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Web Scraping: Building Amazon Auto Scraper
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Job Board Data Web Scraping Automation With Python
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Automate Spotify & Youtube with Python
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Uber Data Analysis With Python
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3RD MONTH
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STATISTICS FOR DATA ANALYTICS
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WEEK 1 :: Master Statistics For Data Analytics
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Statistical Methods Deep Dive
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Data
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Frequency Distribution
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Central Tendency
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Measures of Dispersion
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Coefficient of Variations
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The Five Number Summary & The Quartiles
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The Normal Distribution
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Correlation
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WEEK 2 :: Probability
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Baye's Theorem
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Hypothesis Testing
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- Introduction To Hypothesis (3:50)
- Null Vs Alternative Hypothesis (1:34)
- Setting Up Null and Alternative Hypothesis (0:37)
- One-tailed Vs Two-tailed test (1:40)
- Key Points On Hypothesis Testing (3:08)
- Type 1 vs Type 2 Errors (6:01)
- Process Of Hypothesis testing (1:46)
- P-Value (2:28)
- Alpha-Value or Alpha Level (3:15)
- Confidence Level (2:00)
PROJECT: Statistics For Data Analytics
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R PROGRAMMING FOR DATA ANALYTICS
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- Introduction to R Programming for Data Analytics (6:03)
- R programming Installation (2:54)
- The R Environments (3:34)
- Introduction to RStudio (5:20)
- Getting to Know the RStudio Environment (8:47)
- Working with Raw Data in R Comments in R (13:33)
- Install Packages in R (16:01)
- Tidyverse Package (3:28)
- The Piping Command (5:06)
- Loading Inbuilt Datasets (8:48)
- Loading External Datasets (6:29)
- Using colors in R (10:05)
- Creating bar charts (10:46)
- Creating histograms (3:10)
- Creating box plots (6:30)
- Creating scatterplots (11:08)
- Creating line charts (14:50)
- Creating cluster charts (9:14)
- Selecting cases and subgroups (5:52)
- Recoding variables (6:39)
- Computing new variables (3:02)
- Computing frequencies (6:00)
- Computing descriptives (6:47)
- Computing correlations (7:04)
- Computing contingency tables (7:51)
Microsoft Power BI
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after you enroll
- Lecture resources
- Power BI: An Introduction (11:50)
- Installation (4:46)
- Query Editor Overview (5:03)
- Connectors and Get Data Into Power BI (31:45)
- Clean up Messy Data (PART 1) (15:33)
- Clean up Messy Data (PART 2) (6:20)
- Clean up Messy Data (PART 3) (1:11)
- Creating Relationships (8:40)
- Explore Data Using Visuals (12:18)
- Analyzing Multiple Data Tables Together (4:47)
- Writing DAX Measure (Implicit vs. Explicit Measures) (6:41)
- Calculated Column (6:10)
- Measure vs. Calculated Column (7:36)
- Hybrid Measures (9:28)
- The 80/20 Rule (1:43)
- Text, Image, Cards, Shape (13:33)
- Conditional Formatting (4:02)
- Line Chart, Bar Chart (4:41)
- Top 10 Products/Customers (8:23)
DATA STORY TELLING & PRESENTATION SKILLS
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- Lecture resources
- Introduction to story telling and data presentation (2:21)
- Defining a Story (4:39)
- Making Connections (3:25)
- Story Helpers (2:52)
- The 3 Phases of a Story (3:10)
- Include plot : The 7 plots (3:15)
- Create A Character (2:46)
- Know Your Audience : The Warm Up Room (1:11)
- The 5 Types of Audience (1:58)
- Believe In Your Story (0:44)
- Work with data (3:12)
Github for Data Analytics
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PROJECT: Youtube Video Analysis
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Nutritional Analysis On McDonald's menu
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ANALYSIS OF AMERICAN UNIVERSITIES
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Australian Shopping Cart Analysis
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Recommended Projects
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INTERNSHIPS & JOBS
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