𝐅𝐫𝐞𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐚𝐝𝐝 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞 𝐢𝐧 𝟐𝟎𝟐𝟑
👇👇
https://t.me/udacityfreecourse/118

Nice to see the amazing response from you guys 1600+ voted for free certifications. So here we go :

Alteryx: https://community.alteryx.com/t5/Certification-Exams/bd-p/product-certification

Python: https://www.freecodecamp.org/learn/data-analysis-with-python/
https://www.hackerrank.com/skills-verification/python_basic

Data Visualization: https://www.freecodecamp.org/learn/data-visualization/#data-visualization-with-d3

SQL: https://www.hackerrank.com/skills-verification/sql_basic
https://www.hackerrank.com/skills-verification/sql_intermediate
https://hackerrank.com/skills-verification/sql_advanced

Join @sqlspecialist for more useful resources to become a data analyst

Hope it helps :)

Important SQL concepts to become a data analyst
👇👇
https://www.linkedin.com/posts/sql-analysts_data-analysts-activity-7111254842974613504-X0cj

Important Python concepts to become a data analyst
👇👇
https://www.linkedin.com/posts/sql-analysts_python-for-data-analysis-activity-7111251746722623488-bff0?utm_source=share&utm_medium=member_android

Glad to see the amazing response from you guys 😄

Here are the answers to these questions

Explain the Data Analysis Process:
The data analysis process typically involves several key steps. These steps include:
Data Collection: Gathering the relevant data from various sources.
Data Cleaning: Removing inconsistencies, handling missing values, and ensuring data quality.
Data Exploration: Using descriptive statistics, visualizations, and initial insights to understand the data.
Data Transformation: Preprocessing, feature engineering, and data formatting.
Data Modeling: Applying statistical or machine learning models to extract patterns or make predictions.
Evaluation: Assessing the model's performance and validity.
Interpretation: Drawing meaningful conclusions from the analysis.
Communication: Presenting findings to stakeholders effectively.

What is the Difference Between Descriptive and Inferential Statistics?:
Descriptive statistics summarize and describe data, providing insights into its main characteristics. Examples include measures like mean, median, and standard deviation.
Inferential statistics, on the other hand, involve making predictions or drawing conclusions about a population based on a sample of data. Hypothesis testing and confidence intervals are common inferential statistical techniques.

How Do You Handle Missing Data in a Dataset?:
Handling missing data is crucial for accurate analysis:
I start by identifying the extent of missing data.
For numerical data, I might impute missing values with the mean, median, or a predictive model.
For categorical data, I often use mode imputation.
If appropriate, I consider removing rows with too much missing data.
I also explore if the missingness pattern itself holds valuable information.

What is Exploratory Data Analysis (EDA)?:
EDA is the process of visually and statistically exploring a dataset to understand its characteristics:
I begin with summary statistics, histograms, and box plots to identify data trends.
I create scatterplots and correlation matrices to understand relationships.
Outlier detection and data distribution analysis are also part of EDA.
The goal is to gain insights, identify patterns, and inform subsequent analysis steps.

Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem:
In a previous role, I worked for an e-commerce company, and we wanted to reduce shopping cart abandonment rates. I conducted a data analysis project:
Collected user data, including browsing behavior, demographics, and purchase history.
Cleaned and preprocessed the data.
Explored the data through visualizations and statistical tests.
Built a predictive model to identify factors contributing to cart abandonment.
Found that longer page load times were a significant factor.
Proposed optimizations to reduce load times, resulting in a 15% decrease in cart abandonment rates over a quarter.

Hope it helps :)

5⃣ Project ideas for a data analyst in the investment banking domain

M&A Deal Analysis: Analyze historical mergers and acquisitions (M&A) data to identify trends, such as deal size, industries involved, or geographical regions. Create visualizations and reports to assist in making informed investment decisions.

Risk Assessment Model: Develop a risk assessment model using financial indicators and market data. Predict potential financial risks for investment opportunities, such as stocks, bonds, or startups, and provide recommendations based on risk levels.

Portfolio Performance Analysis: Evaluate the performance of investment portfolios over time. Calculate key performance indicators (KPIs) like Sharpe ratio, alpha, and beta to assess how well portfolios are performing relative to the market.

Sentiment Analysis for Trading: Use natural language processing (NLP) techniques to analyze news articles, social media posts, and financial reports to gauge market sentiment. Develop trading strategies based on sentiment analysis results.

IPO Analysis: Analyze data related to initial public offerings (IPOs), including company financials, industry comparisons, and market conditions. Create a scoring system or model to assess the potential success of IPO investments.

Hope it helps :)

If you are new to data analytics domain and not sure what to do, then my honest recommendation would be to start learning SQL & Excel. If not sure from where to learn then I already shared a lot of resources in this channel, just pick up one and stick to it. Don't start something new until you finish it. Hope it helps :)

Alright! I got a lot of responses from you guys and I will try to reply for most of the concerns in this post

New to Data Analytics, want to know how to start? Then here you go 😄👇

Learn SQL & Excel first and then only if you still have some time go for Power BI/ Tableau to improve your visualization skills. If you are also interested in learning a programming language, then go for Python.

Freecodecamp & Mode are very good resources to learn these skills.

I already shared some really good resources in this channel like: https://t.me/sqlspecialist/398

Again emphasizing you all to learn SQL if still confused.

If you want to practice coding Python/ SQL questions, then go with Leetcode or Hackerrank

Math/ Statistics is important but even if you aren't good with that, its absolutely fine. If you have time, then go to khanacademy where you'll find pretty useful stuff.

You can find more useful resources in these dedicated channels

Excel
👇👇
https://t.me/excel_analyst

Power BI/ Tableau
👇👇
https://t.me/PowerBI_analyst/2

SQL
👇👇
https://t.me/sqlanalyst/29

Python
👇👇
https://t.me/pythonanalyst

Statistics Book
👇👇
https://t.me/DataAnalystInterview/34

Free Certificates for data analysis
👇👇
https://t.me/sqlspecialist/433

Hope I answered most of your questions but let me know if you need any help.

Happy learning :)

Build Data Analyst Portfolio in 1 month

Path 1 (More focus on SQL & then on Python)
👇👇

Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts.
Days 4-7: Dive into SQL basics for data retrieval and manipulation.
Free Resources: https://t.me/sqlanalyst/74

Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings.

Week 3: Intermediate Skills
Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation.
Days 22-23: Explore more advanced SQL topics.

Week 4: Portfolio Completion
Days 24-28: Continue working on your SQL-based projects, applying what you've learned.
Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities.
Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code.

Hope it helps :)

Path 2 (More Focus on Python)
👇👇
Free Resources: https://t.me/pythonanalyst/102

Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts and tools. Focus on Python for data analysis, using libraries like Pandas and Matplotlib.
Days 4-7: Dive into SQL basics for data retrieval and manipulation. There are many free online resources and tutorials available.

Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects. Start with small datasets from sources like Kaggle or publicly available datasets. Analyze the data, create visualizations, and document your findings. Make use of Jupyter Notebooks for your projects.

Week 3: Intermediate Skills
Days 15-21: Explore more advanced topics such as data cleaning, feature engineering, and statistical analysis. Learn about more advanced visualization libraries like Seaborn and Plotly.
Days 22-23: Start a personal project that relates to your interests. This could be related to a hobby or a topic you're passionate about.

Week 4: Portfolio Completion
Days 24-28: Continue working on your personal project, applying what you've learned. Make sure your project has clear objectives, data analysis, visualizations, and conclusions.
Day 29: Create a portfolio website using platforms like GitHub Pages, where you can showcase your projects along with explanations and code.
Day 30: Write a blog post summarizing your journey and the key lessons you've learned during this intense month.

Throughout the month, engage with online communities and forums related to data analysis to seek help when needed and learn from others. Remember, building a portfolio is not just about quantity but also about the quality of your work and your ability to articulate your analysis effectively.

While this plan is intensive, it's essential to manage expectations. You may not become an expert data analyst in a month, but you can certainly create a portfolio that demonstrates your enthusiasm, dedication, and foundational skills in data analysis, which can be a valuable starting point for your career.

Hope it helps :)

Top 5 Interview Questions for Data Analyst
👇👇

1. Can you explain the difference between INNER JOIN and LEFT JOIN in SQL? Provide an example.

Answer: INNER JOIN returns only the rows where there is a match in both tables, while LEFT JOIN returns all rows from the left table and the matched rows from the right table. For example, if we have two tables 'Employees' and 'Departments,' an INNER JOIN would return employees who belong to a department, while a LEFT JOIN would return all employees and their department information, if available.

2. How would you read a CSV file into a Pandas DataFrame using Python?

Answer: You can use the pandas.read_csv() function to read a CSV file into a DataFrame.

3. What is Alteryx, and how can it be used in data preparation and analysis? Share an example of a workflow you've created with Alteryx.
Answer: Alteryx is a data preparation and analytics tool. It allows users to build data workflows visually. For example, I've used Alteryx to create a data cleansing workflow that removes duplicates, handles missing values, and transforms data into a usable format. This streamlined the data preparation process and saved time.

4. How do you handle missing data in a Pandas DataFrame? Explain some common methods for data imputation.

Answer: Missing data can be handled using methods like df.dropna() to remove rows with missing values, or df.fillna() to fill missing values with a specified value or a calculated statistic like the mean or median. For example, to fill missing values with the mean of a column:
df['column_name'].fillna(df['column_name'].mean(), inplace=True)

5. Discuss the importance of data visualization in data analysis. Can you give an example of a visualization you've created to convey insights from a dataset?

Answer: Data visualization is crucial because it helps convey complex information in a visually understandable way. For instance, I created a bar chart to show the sales performance of different products over the past year. This visualization clearly highlighted the best-selling products and allowed stakeholders to make informed decisions about inventory and marketing strategies.

Hope it helps :)

SQL Interview Book
👇👇
https://t.me/DataAnalystInterview/49

Data Analyst Jobs
👇👇
https://t.me/jobs_SQL

Resume tips for someone applying for a Data Analyst role

As I got so many requests in dm who needed some tips to improve their resume, so here you go 😄👇

Tailor Your Resume:
Customize your resume for each job application. Highlight skills and experiences that align with the specific job requirements mentioned in the job posting.

Clear and Concise Summary(optional):
Include a brief, clear summary or objective statement at the beginning of your resume to convey your career goals and what you can offer as a Data Analyst.

Highlight Relevant Skills:
Emphasize technical skills such as SQL, Python, data visualization tools (e.g., Tableau, Power BI), statistical analysis, and data cleaning techniques.

Showcase Data Projects:
Include a section highlighting specific data analysis projects you've worked on. Describe the problem, your approach, tools used, and the outcomes or insights gained.

Quantify Achievements:
Whenever possible, use quantifiable metrics to showcase your accomplishments. For example, mention how your analysis led to a specific percentage increase in revenue or efficiency improvement

Education and Certifications:
List your educational background, including degrees, institutions, and graduation dates. Mention relevant certifications or online courses related to data analysis.

Work Experience:
Detail your relevant work experience, including company names, job titles, and dates. Highlight responsibilities and achievements that demonstrate your data analysis skills.

Keywords and Buzzwords:
Use relevant keywords and industry-specific buzzwords in your resume, as many employers use applicant tracking systems (ATS) to scan resumes for key terms.

Use Action Verbs:
Start bullet points with strong action verbs (e.g., "analyzed," "implemented," "developed") to describe your contributions and responsibilities.

Formatting and Readability:
Keep your resume clean and well-organized. Use a professional font and maintain consistent formatting throughout. Avoid excessive jargon.

Include a LinkedIn Profile:
If you have a LinkedIn profile, consider adding a link to it on your resume. Make sure your LinkedIn profile is complete and showcases your data analysis skills.

Proofread Carefully:
Review your resume for spelling and grammatical errors. Ask a friend or colleague to proofread it as well. Attention to detail is crucial in data analysis.

Keep it to the Point:
Aim for a concise resume that is typically one to two pages long. Focus on what's most relevant to the job you're applying for.

Remember that your resume is your first opportunity to make a strong impression on potential employers. Tailoring it to the job and showcasing your skills and achievements effectively can significantly increase your chances of landing a Data Analyst position.

Hope it helps :)

Stepwise guide to work on data analysis projects

Choose a Topic: Select an area of interest.
Find a Dataset: Locate relevant data.
Data Exploration: Understand the data's structure.
Data Cleaning: Address missing data and outliers.
Exploratory Data Analysis (EDA): Discover patterns and relationships.
Hypotheses: Formulate questions to answer.
Data Analysis: Apply statistical or ML methods.
Visualize Results: Create clear visualizations.
Interpret Findings: Explain what you've discovered.
Conclusion: Summarize key insights.
Communication: Present results effectively.
Share Your Work: Showcase on platforms.
Feedback and Iterate: Learn and improve.

Hope it helps :)

Top 10 Excel functions for data analysis

SUMIF/SUMIFS: Sum values based on specified conditions, allowing you to aggregate data selectively.
AVERAGE: Calculate the average of a range of numbers, useful for finding central tendencies.
COUNT/COUNTIF/COUNTIFS: Count the number of cells that meet specific criteria, helping with data profiling.
MAX/MIN: Find the maximum or minimum value in a dataset, useful for identifying extremes.
IF/IFERROR: Perform conditional calculations and handle errors in data gracefully.
VLOOKUP/HLOOKUP: Search for a value in a table and return related information, aiding data retrieval.
PivotTables: Dynamically summarize and analyze data, making it easier to draw insights.
INDEX/MATCH: Retrieve data based on criteria, providing more flexible lookup capabilities than VLOOKUP.
TEXT and DATE Functions: Manipulate text strings and work with date values effectively.
Statistical Functions (e.g., AVERAGEIFS, STDEV, CORREL): Perform advanced statistical analysis on your data.

These functions form the foundation for many data analysis tasks in Excel and are essential for anyone working data regularly.

Hope it helps :)

Top 10 Python functions that are commonly used in data analysis

import pandas as pd: This function is used to import the Pandas library, which is essential for data manipulation and analysis.

read_csv(): This function from Pandas is used to read data from CSV files into a DataFrame, a primary data structure for data analysis.

head(): It allows you to quickly preview the first few rows of a DataFrame to understand its structure.

describe(): This function provides summary statistics of the numeric columns in a DataFrame, such as mean, standard deviation, and percentiles.

groupby(): It's used to group data by one or more columns, enabling aggregation and analysis within those groups.

pivot_table(): This function helps in creating pivot tables, allowing you to summarize and reshape data for analysis.

fillna(): Useful for filling missing values in a DataFrame with a specified value or a calculated one (e.g., mean or median).

apply(): This function is used to apply custom functions to DataFrame columns or rows, which is handy for data transformation.

plot(): It's part of the Matplotlib library and is used for creating various data visualizations, such as line plots, bar charts, and scatter plots.

merge(): This function is used for combining two or more DataFrames based on a common column or index, which is crucial for joining datasets during analysis.

These functions are essential tools for any data analyst working with Python for data analysis tasks.

Hope it helps :)

Top 10 SQL statements & functions used for data analysis

SELECT: To retrieve data from a database.
FROM: To specify the table or tables from which to retrieve data.
WHERE: To filter data based on specified conditions.
GROUP BY: To group rows with similar values into summary rows.
HAVING: To filter grouped data based on conditions.
ORDER BY: To sort the result set by one or more columns.
COUNT(): To count the number of rows or non-null values in a column.
SUM(): To calculate the sum of values in a numeric column.
AVG(): To calculate the average of values in a numeric column.
JOIN: To combine data from multiple tables based on a related column.

These SQL statements and functions are fundamental for data analysis and querying relational databases effectively.

Hope it helps :)

Here is a simplified SQL example that summarizes all the functions in one query:

Let's say we have a database of sales transactions and we want to find the top-selling products in the last month.

SELECT product_name, SUM(quantity_sold) AS total_sold
FROM sales
WHERE transaction_date >= DATE_SUB(NOW(), INTERVAL 1 MONTH)
GROUP BY product_name
HAVING total_sold > 100
ORDER BY total_sold DESC
LIMIT 10;

In this single query:
We SELECT the product names and the total quantity sold.
We retrieve data FROM the "sales" table.
We use WHERE to filter transactions from the last month.
We GROUP BY product name to group sales by product.
We use HAVING to filter for products that have sold more than 100 units.
We ORDER BY total quantity sold in descending order.
Finally, we LIMIT the result to the top 10 products.

Preparation guide for SQL: https://t.me/free4unow_backup/536

SQL Interview Book: https://t.me/DataAnalystInterview/49

Hope it helps :)

Free Certificates to become a data Analyst
👇👇
https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k?utm_source=share&utm_medium=member_android

We are very close to 100 likes on this post and 1000 followers. Thank you all for your amazing support 😄❤️

Planning to have another similar post on more free certification for data analysis & data science field :)

I got a lot of request from users asking for help in refining resume. So, I thought to share some valuable tips in this post itself for everyone's benefit.

Here are some few key points to note while refining your resume:

Format and Design: Keep your resume clean and professional. Use a modern and easy-to-read font. Utilize clear headings and bullet points for a structured look.

Contact Information: Include your name, phone number, professional email address, and LinkedIn profile (if applicable) at the top of the resume.

Summary or Objective: Write a concise summary or objective statement that highlights your career goals and what you bring to the table.

Professional Experience: List your work experience in reverse chronological order (most recent first). Use action verbs to describe your accomplishments and focus on quantifiable achievements.

Skills: Highlight relevant technical and soft skills. Tailor this section to the specific job you're applying for.

Education: Include your educational background, listing your most recent degree first. Mention any honors or relevant coursework.

Certifications and Training: If you have relevant certifications or training, list them here.

Projects or Portfolio: Showcase any significant projects or a portfolio of your work if it's relevant to the position.

Keywords: Customize your resume for each job application by incorporating keywords from the job posting. This can help your resume pass through applicant tracking systems (ATS).

Proofread: Carefully proofread your resume for grammar and spelling errors. Consider having someone else review it as well.

Tailor Each Resume: Customize your resume for each job application to emphasize the skills and experiences most relevant to that position.

Quantify Achievements: Whenever possible, use specific numbers or percentages to quantify your achievements. This adds credibility to your claims.

Use Action Words: Start bullet points with strong action verbs like "managed," "achieved," "led," etc.

Keep it Concise: Aim for a resume length of one page for less experienced candidates and up to two pages for more experienced professionals.

Update Regularly: Continuously update your resume to reflect your latest experiences and accomplishments.

Seek Feedback: Don't hesitate to seek feedback from mentors, career advisors, or professional colleagues to improve your resume.

Remember that your resume is your marketing tool, so it should effectively communicate your qualifications and value to potential employers. Tailoring it to each job application and staying up-to-date with current resume trends is crucial for success in 2023.

Hope it helps :)