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 :)
👇👇
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 :)
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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 denoscriptive 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!
🗓️Week 1: Foundation of Data Analytics
◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive 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!
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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
👇👇
https://www.linkedin.com/posts/sql-analysts_learn-tableau-activity-7195275749245784064-9TNf?utm_source=share&utm_medium=member_android
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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://news.1rj.ru/str/learndataanalysis/911
Some you'll find here as well: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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://news.1rj.ru/str/learndataanalysis/911
Some you'll find here as well: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍6❤2😁1
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 ❤️
https://www.linkedin.com/posts/sql-analysts_python-cheatsheet-activity-7196728645044834307-BYgX?utm_source=share&utm_medium=member_android
Like for more ❤️
👍4❤2
We are now a community of 50000+ members on LinkedIn
https://www.linkedin.com/company/sql-analysts/
Thanks for the support ❤️
https://www.linkedin.com/company/sql-analysts/
Thanks for the support ❤️
👍6❤5
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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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.me/jobs_SQL
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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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.me/jobs_SQL
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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 :)
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 :)
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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
👇👇
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.
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.
👍12🔥2
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
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
👍20❤13🤔1
Important Interview Questions for SQL 👇👇
https://www.linkedin.com/posts/sql-analysts_preparing-for-an-sql-interview-here-are-activity-7201945469835476994-lI_m
https://www.linkedin.com/posts/sql-analysts_preparing-for-an-sql-interview-here-are-activity-7201945469835476994-lI_m
👍6❤1
Python for everything
👇👇
https://www.linkedin.com/posts/sql-analysts_many-people-charge-too-much-to-teach-python-activity-7204810034940153857-2Rlh
Like for more ❤️
👇👇
https://www.linkedin.com/posts/sql-analysts_many-people-charge-too-much-to-teach-python-activity-7204810034940153857-2Rlh
Like for more ❤️
❤2🔥2
Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career.
👇👇
https://news.1rj.ru/str/sql_engineer/62
👇👇
https://news.1rj.ru/str/sql_engineer/62
🔥3
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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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.me/jobs_SQL
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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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
● Denoscription: [Brief denoscription 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.me/jobs_SQL
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