Becoming a Data Analyst in 2024 is more difficult than it was a couple of years ago. The competition has grown but so has the demand for Data Analysts!

There are 5 areas you need to excel at to land a career in data. (so punny...)
1. Skills
2. Experience
3. Networking
4. Job Search
5. Education

Let's dive into the first and most important area, skills.

Skills
Every data analytics job will require a different set of skills for their job description. To cover the majority of entry-level positions, you should focus on the core 3 (or 4 if you have time).
- Excel
- SQL
- Tableau or Power BI
- Python or R(optional)
No need to learn any more than this to get started. Start learning other skills AFTER you land your first job and see what data analytics path you really enjoy.
You might fall into a path that doesn't require Python at all and if you took 3 months to learn it, you wasted 3 months. Your goal should be to get your foot in the door.

Experience
So how do you show that you have experience if you have never worked as a Data Analyst professionally? 
It's actually easier than you think! 
There are a few ways you can gain experience. volunteer, freelance, or any analytics work at your current job.
First ask your friends, family, or even Reddit if anyone needs help with their data.
Second, you can join Upwork or Fiverr to land some freelance gigs to gain great experience and some extra money.
Thirdly, even if your title isn't "Data Analyst", you might analyze data anyway. Use this as experience!

Networking
I love this section the most. It has been proven by everyone I have mentored that this is one of the most important areas to learn.
Start talking to other Data Analysts, start connecting with the RIGHT people, start posting on LinkedIn, start following people in the field, and start commenting on posts.
All of this, over time, will continue to get "eyes" on your profile. This will lead to more calls, interviews, and like the people I teach, job offers. 
Consistency is important here.

Job Search
I believe this is not a skill and is more like a "numbers game". And the ones who excel here, are the ones who are consistent.
I'm not saying you need to apply all day every day but you should spend SOME time applying every day.
This is important because you don't know when exactly a company will be posting their job posting. You also want to be one of the first people to apply so that means you need to check the job boards in multiple small chunks rather than spend all of your time applying in a single chunk of time.
The best way to do this is to open up all of the filters and select the most recent and posted within the last 3 days. 

Education
If you have a degree or are currently on your way to getting one, this section doesn't really apply to you since you have a leg up on a lot more job opportunities.

So how else does someone show they are educated enough to become a Data Analyst?
You need to prove it by taking relevant courses in relation to the industry you want to enter. After the course, the actual certificate does not hold much weight unless it's an accredited certificate like a Tableau Professional Certificate. 

To counter this, you need to use your project descriptions to explain how you used data to solve a business problem and explain it professionally.

There are so many other areas you could work on but focussing on these to start will definitely get you going in the right direction. 

Take time to put these actions to work. Pivot when something isn't working and adapt.
It will take time but these actions will reduce the time it takes you to become a Data Analyst in 2024

Numpy and Pandas

This slide explores some of the most important Python libraries needed for machine learning

Data Analytics with Spark Using Python
Jeffrey Aven, 2018

If you've mastered Excel, SQL, and Power BI or Tableau, you've learned half of the knowledge needed to be a great data analyst.

We talk a lot about our tech stacks because tech skills are measurable--either you know how to use window functions, or you don't; either you've written a Python script, or you haven't.

But as a data analyst, your value is about 50% tech and 50% analytical thinking. Can you identify a problem, generate a roadmap to the solution, and provide actionable advice? Can you build a dashboard that helps solves business problems, and is not just a collection of metrics?

Tech skills can be learned relatively quickly, but your analytical skills will set you apart from the other applicants.

SQL ROADMAP 👇👇
https://www.linkedin.com/posts/sql-analysts_sql-dataanalytics-datascience-activity-7191432427716653056-k3im?utm_source=share&utm_medium=member_android

SQL Cheat Sheet

Free Statistics Courses for Data Analysis By Udacity👇👇

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Data Analysis in Microsoft Excel
Alex Holloway, 2023

To become a successful data analyst, you need a combination of technical skills, analytical skills, and soft skills. Here are some key skills required to excel in a data analyst role:

1. Statistical Analysis: Understanding statistical concepts and being able to apply them to analyze data sets is essential for a data analyst. Knowledge of probability, hypothesis testing, regression analysis, and other statistical techniques is important.

2. Data Manipulation: Proficiency in tools like SQL for querying databases and manipulating data is crucial. Knowledge of data cleaning, transformation, and preparation techniques is also important.

3. Data Visualization: Being able to create meaningful visualizations using tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn is essential for effectively communicating insights from data.

4. Programming: Strong programming skills in languages like Python or R are often required for data analysis tasks. Knowledge of libraries like Pandas, NumPy, and scikit-learn in Python can be beneficial.

5. Machine Learning(optional): Understanding machine learning concepts and being able to apply algorithms for predictive modeling, clustering, and classification tasks is becoming increasingly important for data analysts.

6. Database Management: Knowledge of database systems like MySQL, PostgreSQL, or MongoDB is useful for working with large datasets and understanding how data is stored and retrieved.

7. Critical Thinking: Data analysts need to be able to think critically and approach problems analytically. Being able to identify patterns, trends, and outliers in data is important for drawing meaningful insights.

8. Business Acumen: Understanding the business context and objectives behind the data analysis is crucial. Data analysts should be able to translate data insights into actionable recommendations for business decision-making.

9. Communication Skills: Data analysts need to effectively communicate their findings to non-technical stakeholders. Strong written and verbal communication skills are essential for presenting complex data analysis results in a clear and understandable manner.

10. Continuous Learning: The field of data analysis is constantly evolving, so a willingness to learn new tools, techniques, and technologies is important for staying current and adapting to changes in the industry.

By developing these skills and gaining practical experience through projects or internships, you can build a strong portfolio for a successful career as a data analyst.

Skills Required For A Data Analyst
👇👇

Basic Excel- 
https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_ADx6nJcoTM5t2S1bmsMdm

Advanced Excel- 
https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_e9xmZNPACsLdgie3Tkaxf

SQL-
https://www.youtube.com/watch?v=5JCyiutyu_o&list=PLmQAMKHKeLZ-kD9VN0prfKCByr9pa4jw6

SQL- (Khan Academy)-
https://www.khanacademy.org/computing/computer-programming/sql

Python Programming Language- 
https://www.youtube.com/watch?v=bPrmA1SEN2k&list=PLZoTAELRMXVNUL99R4bDlVYsncUNvwUBB

Stats Lectures- 
https://www.youtube.com/watch?v=zRUliXuwJCQ&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO

Stats Lectures(Khans Academy)-
https://www.khanacademy.org/math/statistics-probability

Python EDA- 
https://www.youtube.com/playlist?list=PLZoTAELRMXVPQyArDHyQVjQxjj_YmEuO9

Python Feature Engineering- 
https://www.youtube.com/playlist?list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN

Tableau- 
https://www.tableau.com/academic/student-Iron-Viz

Hope it helps you :)

Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:

🗓️Week 1: Foundation of Data Analytics

◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand descriptive statistics, types of data, and data distributions.

◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.

◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.

🗓️Week 2: Intermediate Data Analytics Skills

◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.

◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.

◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.

🗓️Week 3: Advanced Techniques and Tools

◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.

◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.

◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.


🗓️Week 4: Projects and Practice

◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.

◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.


◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.

👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science

Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!

SQL Best Practices

https://www.linkedin.com/posts/sql-analysts_best-practices-to-remember-while-writing-activity-7194558022906503168-7txr?utm_source=share&utm_medium=member_android

Best practices to follow while creating Tableau dashboards
👇👇
https://www.linkedin.com/posts/sql-analysts_learn-tableau-activity-7195275749245784064-9TNf?utm_source=share&utm_medium=member_android

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.

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

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

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

5. 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 👇👇
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Like if it helps :)

This post is for freshers who get confused with the interview questions for the data roles.

Best tip from my side would be to start focusing on your SQL skills. Most of the data roles ask SQL questions based on joins & aggregate functions. Some interviewers may also ask questions based on window function. But, make your basics solid and practice it well.

If you are from non-coding background focus on your excel and bi skills. Learn vlookups, hlookups, pivot table, pivot charts and questions based on basic formulas.

But whatever the case is, stay resilient and believe on yourself. If unsure, start applying for jobs & give interviews. Even if you don't know the answers, don't worry. Even you don't crack the interview, don't worry. It's all part of this journey and you'll become better version of yourself with every small improvement.

Some resources I already shared on this channel: https://www.tg-me.com/learndataanalysis/911

Some you'll find here as well: https://www.tg-me.com/sqlspecialist

Hope it helps :)

I have created this 100-Day Roadmap & Resources for Data Analytics today 👇👇
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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 job search journey... All the best!👍✌️