5 Data Analytics Project Ideas to boost your resume:

1. Stock Market Portfolio Optimization

2. YouTube Data Collection & Analysis

3. Elections Ad Spending & Voting Patterns Analysis

4. EV Market Size Analysis

5. Metro Operations Optimization

Basic SQL Commands ✅

Free Session to learn Data Analytics, Data Science & AI
👇👇
https://tracking.acciojob.com/g/PUfdDxgHR

Register fast, only for first few users

Hi guys,

Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.

For those of you who are new to this channel, here are some quick links to navigate this channel easily.

Data Analyst Learning Plan ?
https://t.me/sqlspecialist/752

Python Learning Plan ?
https://t.me/sqlspecialist/749

Power BI Learning Plan ?
https://t.me/sqlspecialist/745

SQL Learning Plan ?
https://t.me/sqlspecialist/738

SQL Learning Series ?
https://t.me/sqlspecialist/567

Excel Learning Series ?
https://t.me/sqlspecialist/664

Power BI Learning Series ?
https://t.me/sqlspecialist/768

Python Learning Series ?
https://t.me/sqlspecialist/615

Tableau Essential Topics ?
https://t.me/sqlspecialist/667

Best Data Analytics Resources ?
https://heylink.me/DataAnalytics

You can find more resources on Medium & Linkedin

Like for more ❤️

Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.

Hope it helps :)

Data Analyst vs Data Engineer vs Data Scientist ✅

Skills required to become a Data Analyst ?

- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.

Skills required to become a Data Engineer: ?

- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.

Skills required to become a Data Scientist: ?

- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.

Bonus Skills Across All Roles:

- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.

I have curated best 80+ top-notch Data Analytics Resources ??
https://topmate.io/analyst/861634

Like this post for more content like this ?♥️

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

Hope it helps :)

Quick Recap of Essential SQL Concepts

1️⃣ FROM clause: Specifies the tables from which data will be retrieved.
2️⃣ WHERE clause: Filters rows based on specified conditions.
3️⃣ GROUP BY clause: Groups rows that have the same values into summary rows.
4️⃣ HAVING clause: Filters groups based on specified conditions.
5️⃣ SELECT clause: Specifies the columns to be retrieved.
6️⃣ WINDOW functions: Functions that perform calculations across a set of table rows.
7️⃣ AGGREGATE functions: Functions like COUNT, SUM, AVG that perform calculations on a set of values.
8️⃣ UNION / UNION ALL: Combines the result sets of multiple SELECT statements.
9️⃣ ORDER BY clause: Sorts the result set based on specified columns.
? LIMIT / OFFSET (or FETCH / OFFSET in some databases): Controls the number of rows returned and starting point for retrieval.

Here you can find quick SQL Revision Notes?
https://topmate.io/analyst/864817

Hope it helps :)

Top 5 Tools to master Data Analytics
1. Python:
- Versatile programming language.
- Offers powerful libraries like Pandas, NumPy, and Scikit-learn.
- Used for data manipulation, analysis, and machine learning tasks.

1. R:
   - Statistical programming language.
   - Provides extensive statistical capabilities.
   - Popular for data analysis in academia.
   - Offers visualization libraries like ggplot2.

2. SQL (Structured Query Language):
   - Essential for working with relational databases.
   - Allows querying, manipulation, and management of data.
   - Standard language for database management systems.

3. Tableau:
   - Data visualization tool.
   - Enables creation of interactive dashboards.
   - Helps in communicating insights effectively.
   - Widely used in business intelligence.

4. Apache Spark:
   - Framework for large-scale data processing.
   - Offers distributed computing capabilities.
   - Libraries like Spark SQL and MLlib for data manipulation and machine learning.
   - Ideal for processing big data efficiently.

I have curated best 80+ top-notch Data Analytics Resources ??
https://topmate.io/analyst/861634

Like if it helps :)

Don't make this mistake as a beginner data analyst:

Not learning SQL

There's a reason it's been around for 40+ years.

Get started with:

- SQL basics (syntax + structure)
- Data Manipulation (JOINs, GROUP BY etc)
- Aggregation Functions (SUM, AVG etc)

Hey guys ?

Since many of you requested for data analytics recorded video lectures, here you go!
??
https://topmate.io/analyst/1068350

It contains comprehensive recorded video lectures on Data Analytics, covering key tools and languages like SQL, Python, Excel, and Power BI along with hands-on projects to ensure you gain practical experience alongside theoretical knowledge.

Please use the above link to avail them!?

NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.

Hope this helps in your data analytics journey... All the best!?✌️

Must Study: These are the important Questions for Data Analyst ✅

SQL
1. How do you handle NULL values in SQL queries, and why is it important?
2. What is the difference between INNER JOIN and OUTER JOIN, and when would you use each?
3. How do you implement transaction control in SQL Server?

Excel
1. How do you use pivot tables to analyze large datasets in Excel?
2. What are Excel's built-in functions for statistical analysis, and how do you use them?
3. How do you create interactive dashboards in Excel?

Power BI
1. How do you optimize Power BI reports for performance?
2. What is the role of DAX (Data Analysis Expressions) in Power BI, and how do you use it?
3. How do you handle real-time data streaming in Power BI?

Python
1. How do you use Pandas for data manipulation, and what are some advanced features?
2. How do you implement machine learning models in Python, from data preparation to deployment?
3. What are the best practices for handling large datasets in Python?

Data Visualization
1. How do you choose the right visualization technique for different types of data?
2. What is the importance of color theory in data visualization?
3. How do you use tools like Tableau or Power BI for advanced data storytelling?

I have curated best 80+ top-notch Data Analytics Resources ??
https://topmate.io/analyst/861634

Hope this helps you ?