Data Analytics Dream Song
👇👇
https://youtu.be/WCTBf7zfq9M?si=76NhVgI0TaB4v_OJ

If you're thinking about building a data analytics projects, you don't need another book, video, or blog post.

Just start.

You'll learn 10x more by failing big time than by reading someone else's advice 🤷♂️

Starting exploratory data analysis (EDA) can be tricky. Many of us often feel lost at the beginning. Here's a simple way to get on track: start by creating hypothesis questions and defining KPIs based on your dataset and the field you are working in.

𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐞𝐩𝐬 𝐭𝐨 𝐠𝐮𝐢𝐝𝐞 𝐲𝐨𝐮𝐫 𝐄𝐃𝐀:
1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒀𝒐𝒖𝒓 𝑭𝒊𝒆𝒍𝒅: Learn about the industry and the specific problems you're trying to solve. This will help you know what to look for in your data.
2. 𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒚 𝑲𝒆𝒚 𝑴𝒆𝒕𝒓𝒊𝒄𝒔: Decide on the most important KPIs for your analysis. These should align with your business goals and provide clear insights.
3. 𝑪𝒓𝒆𝒂𝒕𝒆 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒆𝒔: Formulate questions that your EDA will try to answer. This keeps your analysis focused and purposeful.

Using these steps will make your EDA process smoother and ensure your results are valuable and relevant.

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 V/S 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (𝐁𝐀):

- Acts as a bridge between the business side and the IT side of an organization.
- Gathers and analyzes business requirements.
- Conducts stakeholder meetings.

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐁𝐈):

- Focuses on data analysis, reporting, and data visualization using BI tools.
- Extracts and transforms data from various sources into meaningful insights to support decision-making.
- Builds dashboards and reports.
- Identifies trends and patterns in data.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞:

𝐀𝐦𝐚𝐳𝐨𝐧: A BA might analyze customer feedback to improve delivery processes, while a BI professional could create dashboards to monitor sales trends and warehouse efficiency.

𝐆𝐨𝐨𝐠𝐥𝐞: A BA could work on improving user experience based on app usage data, whereas a BI expert might analyze advertising data to optimize ad campaigns.

🥳🚀When delving into data analytics and initiating your SQL journey, prioritize mastering the fundamental concepts that address the majority of problems before delving into other topics.

👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX

👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)

👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions

👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS

Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences

👉🏻CTE
👉🏻Views & Triggers (optional)

Here is an amazing resources to learn & practice SQL: https://t.iss.one/sqlanalyst/195

Hope it helps in your SQL learning 📚

Will AI Tools for Data Analysis Replace Data Analysts?

AI and Data Analysis are two closely related scientific areas, that have been developing rapidly for the last several years. As technology continues to evolve, the question arises: Will AI tools for data analysis replace data analysts?

This article aims to describe how AI is related to Data Analysis, what it can do, and will AI tools for data analysis replace data analysts. Starting with the introduction to AI and its fundamental aspects, to how it is going to affect the world in the distant future, the article addresses that and also focuses on how AI is associated with Data analysis.

The moderate generation of AI comprises Machine Learning, Deep Learning, and Generative AI. While generative AI is the capability to produce materials and contents like images, sound, and music, Machine Learning is a specific type of GI that prepares an algorithm to feed information to make a prediction.

Let’s go back to the basics...!

Here’s what you do to become a Data Analyst

- Learn SQL (best skill to have)
- Learn Excel (hidden requirement)
- Learn a BI tool (for nice portfolio projects)

Don’t stop there you still have work to do

- Create a portfolio
- Learn how to create an appealing resume
- Learn how to answer interview questions (STAR method)

After this, my favorite, networking

- Comment on posts
- Start posting yourself
- Reach out to all the recruiters

It can take you anywhere from a couple of months to a year!

It all depends on how much time you can dedicate each day!

But the longer you wait, the longer it will take!

Get after it...!

Complete Guide to Data Analytics for Beginners 👇👇
https://youtu.be/1-T-VBjLpJo?si=fo_RhbXC46Hg-FVE

Since most of you voted for SQL, I created this video which contains essential SQL topics & free resources to practice sql.
👇👇
https://youtu.be/VCZxODefTIs?si=1XB44uv5DIpcJA4K

Please like this video & subscribe my youtube channel so that I can bring more awesome videos. I would really appreciate any feedback in the comments :)

Skills need for everyday data analysis jobs

Guesstimate questions are scary, simply because they really matter for impacting your performance in those all-important interviews — often for consulting, data analytics or product management. No need to worry; you can do it! In this guide, we are looking at how to approach guesstimate questions with confidence and make what sounds like a guessing game into an opportunity for showcasing our analytical thinking.

https://datasimplifier.com/guesstimate-questions/

Reminder for all data analyst job seekers⏰

DA + HR Knowledge➡️HR Analyst

DA + Sales Knowledge➡️Sales Analyst

DA + Supply Chain➡️Supply chain Analyst

DA + Finance Knowledge➡️Finance Analyst

DA + Research Knowledge➡️Research Analyst

DA + Marketing Knowledge➡️Marketing Analyst

What does it mean?

⏩Build more functional / domain knowledge

⏩By doing more projects & research

Why?

✅To increase your chances of landing a DA job 🚀

10 Data Cleaning Techniques Every Data Analyst Should Master:

1. Handling Missing Data

Use methods like imputation (mean, median, mode) or deletion to handle missing values.

In Python, pandas functions like fillna() or dropna() are useful.
 
Example: df.fillna(df.mean()) replaces missing values with the column mean.
 
2. Removing Duplicates

Identify and remove duplicate records to ensure the dataset is accurate. Use drop_duplicates() in pandas.

3. Standardizing Data

Ensure consistency in formatting, such as dates and strings.
Use str.lower() or pd.to_datetime() for standardization.

 
4. Handling Outliers

Detect and manage outliers using statistical methods or by creating visuals like box plots. Methods include capping, flooring, or removing outliers.

Example: df = df[(df['column'] >= lower_limit) & (df['column'] <= upper_limit)]

5. Correcting Data Types

Check that all columns have the correct data types for analysis. Use astype() in pandas to convert data types.

6. Normalizing and Scaling Data

Normalize or scale data to bring all values into a similar range, which is important for algorithms like K-Means clustering.

Use StandardScaler or MinMaxScaler from scikit-learn.

Example: from sklearn.preprocessing import StandardScaler; df_scaled = StandardScaler().fit_transform(df)

7. Encoding Categorical Variables

Convert categorical data into numerical format using techniques like one-hot encoding or label encoding. Use pd.get_dummies() or LabelEncoder.

Example: df_encoded = pd.get_dummies(df, columns=['category'])
 
8. Dealing with Inconsistent Data

Identify and correct inconsistencies in data entries, such as typos or inconsistent naming conventions.

Example: df['column'] = df['column'].replace({'val1':'value1', 'val2':'value2'})