To transition from Data Analyst ➡️ Data Scientist, you will have to focus on building relevant projects! 🎯

✅ Predictive Analytics Project
→ Built a model to predict customer behaviour by analyzing past purchase patterns and used time series forecasting to predict future trends.

✅ Sentiment Analysis using NLP
→ Developed a sentiment analysis model that categorized customer feedback into positive, neutral, and negative sentiments to improve products.

✅ Personalized Recommendation Engine
→ Created a recommendation engine using collaborative and content-based filtering to give personalized suggestions based on user’s browsing history and preferences.

Tailor every project to focus on business impact and user experience, which can help you stand out to recruiters. 💪🏻

Q. Explain the data preprocessing steps in data analysis.

Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.

Q. What Are the Three Stages of Building a Model in Machine Learning?

Ans. The three stages of building a machine learning model are:

Model Building: Choosing a suitable algorithm for the model and train it according to the requirement

Model Testing: Checking the accuracy of the model through the test data

Applying the Model: Making the required changes after testing and use the final model for real-time projects


Q. What are the subsets of SQL?

Ans. The following are the four significant subsets of the SQL:

Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.

Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.

Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.

Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.


Q. What is a Parameter in Tableau? Give an Example.

Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.

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Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

Join for more: https://news.1rj.ru/str/datasciencefun

ENJOY LEARNING 👍👍

🚀 Walk-in Hiring Drive Alert! 🚀

AccioJob x Sceniuz are hiring for Data Analyst & Data Engineer roles!

* Graduation Year: Open to All
* Degree: BTech / BE / BCA / BSC / MTech /ME / MCA / MSC
* CTC: 3–6 LPA
* Offline Assesment at AccioJob partnered campus in Mumbai

👉🏻 Data Analyst: https://go.acciojob.com/47HSHh
👉🏻 Data Engineer: https://go.acciojob.com/PnRTK2

Let's now understand the above Data Analyst Roadmap in detail: 🧠↗️

1️⃣ Learn Excel ⭐️

The foundation of data analysis. Learn formulas, pivot tables, charts, VLOOKUP/XLOOKUP, and conditional formatting. It helps in quick data cleaning and presenting insights.

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

2️⃣ Learn SQL 💻

Essential for working with databases. Focus on SELECT, JOIN, GROUP BY, WHERE, and subqueries to extract and manipulate data from relational databases.

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

3️⃣ Learn Python 📱

A powerful tool for data manipulation and automation. Master libraries like pandas, numpy, matplotlib, and seaborn for data cleaning and visualization.

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

4️⃣ Learn Power BI / Tableau 📈

These tools help create interactive dashboards and visual reports. Learn how to import data, create filters, use DAX (Power BI), and design clear visualizations.

Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

5️⃣ Learn Statistics & Probability 🛍

Know about denoscriptive stats (mean, median, mode), inferential stats, distributions, hypothesis testing, and correlation. Vital for making sense of data trends.

Statistics Resources: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

6️⃣ Learn Data Transformation 📈

Learn how to clean, shape, and prepare data for analysis. Use Python (pandas) or Power Query in Power BI, and understand ETL (Extract, Transform, Load) processes.

Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27

7️⃣ Learn Machine Learning 🧠

Understand basic concepts like regression, classification, clustering, and decision trees. You don’t need to be an ML expert, just grasp how models work and when to use them.

Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O

8️⃣ Build Projects & Portfolio 🏹

Apply what you’ve learned to real datasets—like sales analysis, churn prediction, or dashboard creation. Showcase your work on GitHub or a personal website.

Data Analytics Projects: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29

9️⃣ Apply for Jobs 💼

With your skills and portfolio in place, start applying for data analyst roles. Tailor your resume using keywords from job denoscriptions and prepare to answer SQL and Excel tasks in interviews.

Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

Share with credits: https://news.1rj.ru/str/sqlspecialist

Double Tap ♥️ for more

Your first SQL noscript will confuse even yourself.

Your first Power BI dashboard will look like it's your first dashboard.

Stop trying to perfect your first handful of projects.

Start pumping out projects left and right.

While learning, it's more important to create than to focus on optimizing.

Quantity > Quality

Once you start getting faster, you'll have more time to swap it to.

Quality > Quantity

You'll improve rapidly this way.

Complete Data Analyst Interview Guide (0–2 Years of Experience)

🔹 Round 1: SQL + Scenario-Based Questions

Q1. Get top 3 products by revenue within each category

SELECT *
FROM (
SELECT p.product_id, p.category, SUM(o.revenue) AS total_revenue,
RANK() OVER(PARTITION BY p.category ORDER BY SUM(o.revenue) DESC) AS rnk
FROM products p
JOIN orders o ON p.product_id = o.product_id
GROUP BY p.product_id, p.category
) ranked
WHERE rnk <= 3;

Q2. Find users who purchased in January but not in February

SELECT DISTINCT user_id
FROM orders
WHERE MONTH(order_date) = 1
AND user_id NOT IN (
SELECT user_id FROM orders WHERE MONTH(order_date) = 2
);

Q3. Avg. ride time by city + peak hours

SELECT city, AVG(DATEDIFF(MINUTE, start_time, end_time)) AS avg_ride_mins
FROM trips
GROUP BY city;

-- For peak hour detection (example logic)
SELECT DATEPART(HOUR, start_time) AS ride_hour, COUNT(*) AS ride_count
FROM trips
GROUP BY DATEPART(HOUR, start_time)
ORDER BY ride_count DESC;


⸻

🔹 Round 2: Python + Data Cleaning

Q1. Clean messy CSV with pandas

import pandas as pd

df = pd.read_csv('data.csv')
df.columns = df.columns.str.strip().str.lower()
df.drop_duplicates(inplace=True)
df['date'] = pd.to_datetime(df['date'], errors='coerce')
df.fillna(method='ffill', inplace=True)

Q2. Extract domain names from email IDs

emails = ['abc@gmail.com', 'xyz@outlook.com']
domains = [email.split('@')[1] for email in emails]

Q3. Difference: .loc[] vs .iloc[]
• .loc[] → label-based selection
• .iloc[] → index-based selection

Q4. Handle outliers using IQR

Q1 = df['column'].quantile(0.25)
Q3 = df['column'].quantile(0.75)
IQR = Q3 - Q1
filtered_df = df[(df['column'] >= Q1 - 1.5*IQR) & (df['column'] <= Q3 + 1.5*IQR)]


⸻

🔹 Round 3: Power BI / Dashboarding

Tasks you should know:
• Create a dashboard with weekly trends, margins, churn %
• Use bookmarks/slicers for KPI toggles
• Apply filters to show top 5 items dynamically
• Exclude visuals from slicer using “Edit Interactions” → turn off filter icon on card visual

🔗 Try replicating dashboards from Power BI Gallery

⸻

🔹 Round 4: Business Case + Logic-Based Thinking

Q1. Sales dropped last quarter — what to check?
• Compare YoY/QoQ data
• Identify categories/geos with the biggest drop
• Analyze order volume vs. avg. order value
• Check marketing spend, discounts, stockouts

Q2. App downloads ⬆️, activity ⬇️ — what’s wrong?
• Check Day 1/7/30 retention
• Is onboarding working?
• UI bugs or crashes?
• Compare install → sign-up → usage funnel

Q3. Returns increasing — how to investigate?
• Analyze return % by brand, category, SKU
• Check return reasons (defects, sizing, etc.)
• Compare returners’ order history
• Seasonal impact?

⸻

🔰 Free Practice Tools:
• 🔹 SQL on LeetCode
• 🔹 Python on Hackerrank
• 🔹 Power BI Gallery

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