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

Free Statistics Courses for Data Analysis By Udacity👇👇

https://imp.i115008.net/9WnNy3

https://imp.i115008.net/xkvo2O

https://imp.i115008.net/rQyWzB

ENJOY 😌

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

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://t.iss.one/learndataanalysis/911

Some you'll find here as well: https://t.iss.one/sqlspecialist

Hope it helps :)

Complete Python Topics for Data Analytics 👇👇
https://www.linkedin.com/posts/sql-analysts_python-cheatsheet-activity-7196728645044834307-BYgX?utm_source=share&utm_medium=member_android

Like for more ❤️

We are now a community of 50000+ members on LinkedIn

https://www.linkedin.com/company/sql-analysts/

Thanks for the support ❤️

SAMPLE RESUME TEMPLATE FOR A DATA ANALYST(FRESHER)

Creating a resume as a fresher data analyst involves highlighting your education, skills, projects, and any relevant experience you have gained through internships, coursework, or personal projects.

Here’s a structured resume template tailored for a fresher in data analysis:

[Your Name] [Your Address] [City, State, Zip Code] [Your Email Address] [Your Phone Number] [LinkedIn Profile] [GitHub Profile (if applicable)]

Objective:-
A motivated and detail-oriented data analyst with a strong foundation in statistics, data manipulation, and visualization. Seeking to leverage technical and analytical skills to solve complex problems and drive business insights in an entry-level data analyst role.

Education:-

Bachelor of Science in [Your Major] [Your University], [City, State]
Graduation Date: [Month, Year]

● Relevant Coursework: Data Structures, Statistics, Data Mining, Machine Learning, Database Management, Business Analytics

Technical Skills:-

● Programming Languages: Python, R, SQL

● Data Manipulation: pandas, NumPy

● Data Visualization: matplotlib, seaborn, ggplot2, Tableau, Power BI

● Databases: MySQL, PostgreSQL

● Tools: Excel, Jupyter Notebook, RStudio

● Other Skills: Data Cleaning, Data Wrangling, Exploratory Data Analysis (EDA), Statistical Analysis, Machine Learning Basics

Projects:-

Project Title 1
● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.]
● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.]

Project Title 2
● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.]

● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.]

Project Title 3
● Description: [Brief description of the project, the problem you solved, and the tools/technologies you used.]

● Key Achievements: [Highlight specific outcomes, insights derived, or skills applied.]

Internships and Experience:-

Data Analyst Intern [Company Name], [City, State]
[Month, Year] – [Month, Year]

● Assisted in collecting, cleaning, and analyzing large datasets to support business decision-making.

● Developed dashboards and visualizations to present data insights to stakeholders.

● Conducted statistical analyses to identify trends and patterns in data.
Research Assistant [University Department or Lab], [City, State]
[Month, Year] – [Month, Year]

● Collaborated on research projects involving data collection, data entry, and preliminary data analysis.

● Used statistical software to analyze research data and prepare reports.

Certifications:-

● Google Data Analytics Professional Certificate

● Microsoft Certified: Data Analyst Associate

● [Any other relevant certification]

Extracurricular Activities:-

Member, Data Science Club, [Your University]

● Participated in data analysis competitions and hackathons.

● Attended workshops and seminars on data science and analytics.
Volunteer, [Organization Name]

● Contributed to data-driven projects that helped the organization improve its operations and outreach.

Additional Information:-

● Languages: [Any languages you speak other than English, if applicable]

● Interests: [Relevant interests that can show your passion for data and analysis, e.g., participating in Kaggle competitions, blogging about data science, etc.]

Data Analyst Jobs -> t.iss.one/jobs_SQL

If you're looking to build a career in Data Analytics but feel unsure about where to start, this post is for you.

It's important to know that you don't need to spend money on expensive courses to succeed in this field.

Many posts you see on LinkedIn promoting paid courses are often shared by individuals who are either trying to sell their own products or are being compensated to endorse these courses.

Through this post, I will share with you everything you need to start your data journey absolutely free.

🔗 Source

Hope it helps :)

Step-by-step guide to master data analytics
👇👇
https://www.linkedin.com/posts/sql-analysts_step-by-step-guide-to-master-data-analytics-activity-7200748815463649281-zPT2

Top 5 skills for DataAnalytics

1. Proficiency in programming languages like Python, R, or SQL.
2. Strong analytical and problem-solving skills.
3. Ability to work with data manipulation and visualization tools like Pandas, NumPy, Matplotlib, and Seaborn.
4. Knowledge of statistical analysis and machine learning techniques.
5. Effective communication and storytelling skills to convey insights from data to stakeholders.

Want to become a data analyst?

Stage 1 – Excel
Stage 2 – SQL + Project
Stage 3 – Python (Pandas, NumPy) + Project
Stage 4 – Data Visualization (Matplotlib, Seaborn) + Project
Stage 5 – Statistics + Project
Stage 6 – Machine Learning (Scikit-learn) + Project
Stage 7 – Big Data Tools (Hadoop, Spark) + Project

🏆 – DataAnalytics

Important Interview Questions for SQL 👇👇
https://www.linkedin.com/posts/sql-analysts_preparing-for-an-sql-interview-here-are-activity-7201945469835476994-lI_m