Data Analytics Skills

The most important thing data analysts do is to understand the business requirements.

(1) Gathering Data

This means collecting data from different sources. Many a times this is done in collaboration with data engineers and architects hence usually the data analyst doesn’t have to do a lot in this.

(2) Cleaning Data

Going through the data and trying to understand it, making corrections where needed such as removing outliers or data that should not be included in the analysis. This step can take a lot of time, but understanding the data is crucial before you start to process it.

(3) Processing data

The data processing part of the process is where I use my skills and tools to analyze the work and come up with solutions for the problem at hand.

(4) Creating reports for business leaders

As an analyst, a lot of my time goes into creating and maintaining reports/dashboards for stakeholders and business leaders. This means showing the metrics and KPIs in the best manner possible to help drive business decisions.

The best analysts are those that can use data to tell a story.

(5) Collaborating with people

This one is my favorite! As a data analyst, you work with many people across departments, both senior and junior. You’ll also likely collaborate closely with other people who work in data science like data architects and database developers.

Tools I use: Excel,PowerBI,SQL and Python(sometimes)

I have curated best top-notch Data Analytics Resources 👇👇
https://t.me/dataanalysisresourcestp

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More Resources Here👇
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Excel Functions

Tableau Learning Plan in 2025

|-- Week 1: Introduction to Tableau
| |-- Tableau Basics
| | |-- What is Tableau?
| | |-- Tableau Products Overview (Desktop, Public, Online, Server)
| | |-- Installing Tableau Desktop
| |-- Setting up Tableau Environment
| | |-- Connecting to Data Sources
| | |-- Overview of the Tableau Interface
| | |-- Basic Operations (Open, Save, Close)
| |-- First Tableau Dashboard
| | |-- Creating a Simple Dashboard
| | |-- Basic Charts and Visualizations
| | |-- Adding Filters and Actions
|
|-- Week 2: Data Preparation and Transformation
| |-- Data Connections
| | |-- Connecting to Various Data Sources (Excel, SQL, Web Data)
| | |-- Data Extracts vs. Live Connections
| |-- Data Cleaning and Shaping
| | |-- Data Interpreter
| | |-- Pivot and Unpivot Data
| | |-- Handling Null Values
| |-- Data Blending and Joins
| | |-- Data Blending
| | |-- Joins and Relationships
| | |-- Union Data
|
|-- Week 3: Intermediate Tableau
| |-- Advanced Calculations
| | |-- Calculated Fields
| | |-- Table Calculations
| | |-- Level of Detail (LOD) Expressions
| |-- Advanced Visualizations
| | |-- Dual-Axis Charts
| | |-- Heat Maps and Highlight Tables
| | |-- Custom Geocoding
| |-- Dashboard Interactivity
| | |-- Filters and Parameters
| | |-- Dashboard Actions
| | |-- Using Stories for Narrative
|
|-- Week 4: Data Visualization Best Practices
| |-- Design Principles
| | |-- Choosing the Right Chart Type
| | |-- Color Theory
| | |-- Layout and Formatting
| |-- Advanced Mapping
| | |-- Creating and Customizing Maps
| | |-- Using Map Layers
| | |-- Geographic Data Visualization
| |-- Performance Optimization
| | |-- Optimizing Data Sources
| | |-- Reducing Load Times
| | |-- Extracts and Aggregations
|
|-- Week 5: Tableau for Business Intelligence
| |-- Business Dashboards
| | |-- KPI Dashboards
| | |-- Sales and Revenue Dashboards
| | |-- Financial Dashboards
| |-- Storytelling with Data
| | |-- Creating Data Stories
| | |-- Using Annotations
| | |-- Interactive Dashboards
| |-- Sharing and Collaboration
| | |-- Publishing to Tableau Server/Public
| | |-- Tableau Online Collaboration
| | |-- Embedding Dashboards in Websites
|
|-- Week 6-8: Advanced Tableau Techniques
| |-- Tableau Prep
| | |-- Data Preparation Workflows
| | |-- Cleaning and Shaping Data with Tableau Prep
| | |-- Combining Data from Multiple Sources
| |-- Tableau and Scripting
| | |-- Using R and Python in Tableau
| | |-- Advanced Analytics with Scripting
| |-- Advanced Analytics
| | |-- Forecasting
| | |-- Clustering
| | |-- Trend Lines
| |-- Tableau Extensions
| | |-- Installing and Using Extensions
| | |-- Popular Extensions Overview
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Dashboards
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Tableau and SQL
| | |-- Tableau and Excel
| | |-- Tableau and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Tableau Administration
| | |-- Managing Tableau Server
| | |-- User Roles and Permissions
| | |-- Monitoring and Auditing
| |-- Advanced Tableau Topics
| | |-- New Tableau Features
| | |-- Latest Tableau Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Tableau Official)
| |-- Tableau Blogs and Podcasts
| |-- Tableau Communities

Free Data Analytics Courses: https://t.me/dataanalysisresourcestp/48

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Tools Every AI Engineer Should Know

1. Data Science Tools
* Python: Preferred language with libraries like NumPy, Pandas, Scikit-learn.
* R: Ideal for statistical analysis and data visualization.
* Jupyter Notebook: Interactive coding environment for Python and R.
* MATLAB: Used for mathematical modeling and algorithm development.
* RapidMiner: Drag-and-drop platform for machine learning workflows.
* KNIME: Open-source analytics platform for data integration and analysis.

2. Machine Learning Tools
* Scikit-learn: Comprehensive library for traditional ML algorithms.
* XGBoost & LightGBM: Specialized tools for gradient boosting.
* TensorFlow: Open-source framework for ML and DL.
* PyTorch: Popular DL framework with a dynamic computation graph.
* H2O.ai: Scalable platform for ML and AutoML.
* Auto-sklearn: AutoML for automating the ML pipeline.

3. Deep Learning Tools
* Keras: User-friendly high-level API for building neural networks.
* PyTorch: Excellent for research and production in DL.
* TensorFlow: Versatile for both research and deployment.
* ONNX: Open format for model interoperability.
* OpenCV: For image processing and computer vision.
* Hugging Face: Focused on natural language processing.

4. Data Engineering Tools
* Apache Hadoop: Framework for distributed storage and processing.
* Apache Spark: Fast cluster-computing framework.
* Kafka: Distributed streaming platform.
* Airflow: Workflow automation tool.
* Fivetran: ETL tool for data integration.
* dbt: Data transformation tool using SQL.

5. Data Visualization Tools
* Tableau: Drag-and-drop BI tool for interactive dashboards.
* Power BI: Microsoft’s BI platform for data analysis and visualization.
* Matplotlib & Seaborn: Python libraries for static and interactive plots.
* Plotly: Interactive plotting library with Dash for web apps.
* D3.js: JavaScript library for creating dynamic web visualizations.

6. Cloud Platforms
* AWS: Services like SageMaker for ML model building.
* Google Cloud Platform (GCP): Tools like BigQuery and AutoML.
* Microsoft Azure: Azure ML Studio for ML workflows.
* IBM Watson: AI platform for custom model development.

7. Version Control and Collaboration Tools
* Git: Version control system.
* GitHub/GitLab: Platforms for code sharing and collaboration.
* Bitbucket: Version control for teams.

8. Other Essential Tools
* Docker: For containerizing applications.
* Kubernetes: Orchestration of containerized applications.
* MLflow: Experiment tracking and deployment.
* Weights & Biases (W&B): Experiment tracking and collaboration.
* Pandas Profiling: Automated data profiling.
* BigQuery/Athena: Serverless data warehousing tools.

Mastering these tools will ensure you are well-equipped to handle various challenges across the AI lifecycle.

Machine Learning Algorithms: https://t.me/mlresourcestp/47

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Data Science Projects for Beginners: https://t.me/datascienceresourcestp/65

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Data Analytics Essential Skills

MUST ADD these 5 POWER Bl projects to your resume to get hired

Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger

📌Customer Churn Analysis
🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input

📌Credit Card Fraud
🔗 https://github.com/sahidul-shaikh/credit-card-fraud-

📌Movie Sales Analysis
🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data

📌Airline Sector
🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline-

📌Financial Data Analysis
🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data-

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Simple guide

1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.

2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.

3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.

4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.

5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.

6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.

7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.

Power BI Syllabus: https://t.me/dataanalysisresourcestp/66

Chart Selection: https://t.me/dataanalysisresourcestp/81

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Complete Excel Topics for Data Analysts 😄👇

MS Excel Free Resources
-> https://t.me/dataanalysisresourcestp/92

1. Introduction to Excel:
- Basic spreadsheet navigation
- Understanding cells, rows, and columns

2. Data Entry and Formatting:
- Entering and formatting data
- Cell styles and formatting options

3. Formulas and Functions:
- Basic arithmetic functions
- SUM, AVERAGE, COUNT functions

4. Data Cleaning and Validation:
- Removing duplicates
- Data validation techniques

5. Sorting and Filtering:
- Sorting data
- Using filters for data analysis

6. Charts and Graphs:
- Creating basic charts (bar, line, pie)
- Customizing and formatting charts

7. PivotTables and PivotCharts:
- Creating PivotTables
- Analyzing data with PivotCharts

8. Advanced Formulas:
- VLOOKUP, HLOOKUP, INDEX-MATCH
- IF statements for conditional logic

9. Data Analysis with What-If Analysis:
- Goal Seek
- Scenario Manager and Data Tables

10. Advanced Charting Techniques:
- Combination charts
- Dynamic charts with named ranges

11. Power Query:
- Importing and transforming data with Power Query

12. Data Visualization with Power BI:
- Connecting Excel to Power BI
- Creating interactive dashboards

13. Macros and Automation:
- Recording and running macros
- Automation with VBA (Visual Basic for Applications)

14. Advanced Data Analysis:
- Regression analysis
- Data forecasting with Excel

15. Collaboration and Sharing:
- Excel sharing options
- Collaborative editing and comments

16. Excel Shortcuts and Productivity Tips:
- Time-saving keyboard shortcuts
- Productivity tips for efficient work

17. Data Import and Export:
- Importing and exporting data to/from Excel

18. Data Security and Protection:
- Password protection
- Worksheet and workbook security

19. Excel Add-Ins:
- Using and installing Excel add-ins for extended functionality

20. Mastering Excel for Data Analysis:
- Comprehensive project or case study integrating various Excel skills

Since Excel is another essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this Excel series 👍♥️

Share with credits: https://t.me/sqlresourcestp

Hope it helps :)

Many people charge too much to teach SQL, but my mission is to break down barriers. I have shared complete learning series to learn SQL from scratch.

Here are the links to the SQL series

Complete SQL Topics for Data Analyst: https://t.me/sqlresourcestp/83

Part-1: https://t.me/sqlresourcestp/58

Part-2: https://t.me/sqlresourcestp/59

Part-3: https://t.me/sqlresourcestp/60

Part-4: https://t.me/sqlresourcestp/61

Part-5: https://t.me/sqlresourcestp/62

Part-6: https://t.me/sqlresourcestp/63

Part-7: https://t.me/sqlresourcestp/64

Part-8: https://t.me/sqlresourcestp/65

Part-9: https://t.me/sqlresourcestp/66

Part-10: https://t.me/sqlresourcestp/67

Part-11: https://t.me/sqlresourcestp/68

Part-12: https://t.me/sqlresourcestp/71

Part-13: https://t.me/sqlresourcestp/72

Part-14: https://t.me/sqlresourcestp/73

Part-15: https://t.me/sqlresourcestp/74

Part-16: https://t.me/sqlresourcestp/75

Part-17: https://t.me/sqlresourcestp/76

Part-18: https://t.me/sqlresourcestp/77

Part-19: https://t.me/sqlresourcestp/80

Part-20: https://t.me/sqlresourcestp/81

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Complete Excel Topics for Data Analysts: https://t.me/dataanalysisresourcestp/93

Here you can find SQL Interview Resources👇
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R Programming Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to R
| | |-- Setting Up Development Environment (RStudio)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Repeat Loop
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Warnings and Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments
| | |-- Return Statement
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Environments
|
|-- Data Structures
| |-- Vectors
| | |-- Creating Vectors
| | |-- Vectorized Operations
| |
| |-- Lists
| | |-- Creating and Manipulating Lists
| |
| |-- Matrices
| | |-- Creating Matrices
| | |-- Matrix Operations
| |
| |-- Data Frames
| | |-- Creating Data Frames
| | |-- Manipulating Data Frames
| |
| |-- Factors
| | |-- Creating and Using Factors
|
|-- Data Manipulation
| |-- dplyr
| | |-- Select, Filter, Arrange, Mutate, Summarize
| | |-- Piping (%>%)
| |
| |-- tidyr
| | |-- Gather and Spread
| | |-- Separate and Unite
|
|-- Data Visualization
| |-- Base R Graphics
| | |-- Plot, Hist, Boxplot, Barplot
| |
| |-- ggplot2
| | |-- Grammar of Graphics
| | |-- Creating Plots (Scatter, Line, Bar, Histogram)
| | |-- Customizing Plots (Themes, Labels, Legends)
|
|-- Statistical Analysis
| |-- Descriptive Statistics
| | |-- Mean, Median, Mode
| | |-- Standard Deviation, Variance
| |
| |-- Inferential Statistics
| | |-- Hypothesis Testing (t-tests, ANOVA)
| | |-- Correlation and Regression Analysis
|
|-- Advanced R
| |-- Date and Time
| | |-- Working with Dates and Times
| | |-- lubridate Package
| |
| |-- String Manipulation
| | |-- Stringr Package
| | |-- Regular Expressions
|
|-- Programming Concepts
| |-- Apply Family of Functions
| | |-- lapply, sapply, tapply, vapply
| |
| |-- Debugging
| | |-- Debugging Tools (browser, debug, trace)
| |
| |-- Object-Oriented Programming (OOP)
| | |-- S3 and S4 Systems
| | |-- Reference Classes (R5)
|
|-- Libraries and Packages
| |-- CRAN and Bioconductor
| | |-- Installing and Using Packages
| |
| |-- Popular Packages
| | |-- Data Manipulation (dplyr, tidyr)
| | |-- Data Visualization (ggplot2, lattice)
| | |-- Machine Learning (caret, randomForest)
|
|-- Reporting and Documentation
| |-- RMarkdown
| | |-- Creating RMarkdown Documents
| | |-- Including Code Chunks
| | |-- Generating Reports (HTML, PDF, Word)
|
|-- Deployment and Reproducibility
| |-- Version Control with Git
| | |-- Integrating RStudio with GitHub
| |
| |-- Reproducible Research
| | |-- Workflow Practices
| | |-- Using renv for Package Management
|
|-- Working with Big Data
| |-- Data.table Package
| | |-- Efficient Data Manipulation
| |
| |-- SparkR
| | |-- Using Apache Spark with R
| | |-- Handling Large Datasets

Free R Programming Courses

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Data Analyst Checklist

This is a very COMMON issue that I observe in the projects of aspiring candidates

They download a DATASET from Kaggle or any other website

Export it to a Data Analysis TOOL

And START the project with data cleaning

After cleaning the data, they PLUG it into a dashboard

In the dashboard, they put EVERY column into the visuals

Also they APPLY the filters of top bottom 10

Once done, they crack their KNUCKLES

And put this project in a list of SUCCESSFULLY completed projects

Over time, I have REVIEWED so many portfolio projects

And I see this ISSUES almost every time

When I go to their portfolio, for every project there is a DASHBOARD

But WHAT should I do after seeing a dashboard

What is it trying to SAY

What should I do after SEEING top or bottom 10 cities, states or products

Every dashboard lacks CONTEXT

And why NOT

Because they DON'T even know the business problem or problem statement

So the dashboard you created is of NO use

Your job is not just to create DASHBOARDS

Your job would be to create DASHBOARDS to take out important INSIGHTS

And from those insights, you will build RECOMMENDATIONS

And these recommendations will be given to stakeholders as a SOLUTION to their business problem

If they implemented your IDEAS and the problem gets solved

Now you can say your work is DONE

If you are SHOWING bottom 10 states, then what

You should write the INSIGHTS too

For example, the sales of North India zone are FALLING

The insights can be used like this

Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states

And this might be the REASON why our North India sales are DROPPING so hard

This is just a RANDOM example showing how your charts become UNDERSTANDABLE

Well, everyone can EXTRACT insights from charts

Even a KID can do this after looking at the tallest and smallest bar

The real task is to give RECOMMENDATIONS to solve the BUSINESS problem

And I have NEVER seen this in anyone's portfolio

If you are doing this, then you are easily STANDING out in the crowd

In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations

Even in the bullet point of projects in my resume, I included RECOMMENDATIONS

Now this is what you can call a STRONG portfolio

Because your analysis skills are the SAME as those used in the real life by a Data Analyst

7 Free Data Analytics Courses👇👇
https://t.me/dataanalysisresourcestp/98

Like if it helps 😄

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