The Only roadmap you need to become an ML Engineer ๐ฅณ
Phase 1: Foundations (1-2 Months)
๐น Math & Stats Basics โ Linear Algebra, Probability, Statistics
๐น Python Programming โ NumPy, Pandas, Matplotlib, Scikit-Learn
๐น Data Handling โ Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
๐น Supervised & Unsupervised Learning โ Regression, Classification, Clustering
๐น Model Evaluation โ Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
๐น Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization
๐น Basic ML Projects โ Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
๐น Neural Networks โ TensorFlow & PyTorch Basics
๐น CNNs & Image Processing โ Object Detection, Image Classification
๐น NLP & Transformers โ Sentiment Analysis, BERT, LLMs (GPT, Gemini)
๐น Reinforcement Learning Basics โ Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
๐น ML in Production โ Model Deployment (Flask, FastAPI, Docker)
๐น MLOps โ CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
๐น Cloud & Big Data โ AWS/GCP/Azure, Spark, Kafka
๐น End-to-End ML Projects โ Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
๐น Specialize โ Computer Vision, NLP, Generative AI, Edge AI
๐น Interview Prep โ Leetcode for ML, System Design, ML Case Studies
๐น Portfolio Building โ GitHub, Kaggle Competitions, Writing Blogs
๐น Networking โ Contribute to open-source, Attend ML meetups, LinkedIn presence
Follow this advanced roadmap to build a successful career in ML!
The data field is vast, offering endless opportunities so start preparing now.
Phase 1: Foundations (1-2 Months)
๐น Math & Stats Basics โ Linear Algebra, Probability, Statistics
๐น Python Programming โ NumPy, Pandas, Matplotlib, Scikit-Learn
๐น Data Handling โ Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
๐น Supervised & Unsupervised Learning โ Regression, Classification, Clustering
๐น Model Evaluation โ Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
๐น Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization
๐น Basic ML Projects โ Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
๐น Neural Networks โ TensorFlow & PyTorch Basics
๐น CNNs & Image Processing โ Object Detection, Image Classification
๐น NLP & Transformers โ Sentiment Analysis, BERT, LLMs (GPT, Gemini)
๐น Reinforcement Learning Basics โ Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
๐น ML in Production โ Model Deployment (Flask, FastAPI, Docker)
๐น MLOps โ CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
๐น Cloud & Big Data โ AWS/GCP/Azure, Spark, Kafka
๐น End-to-End ML Projects โ Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
๐น Specialize โ Computer Vision, NLP, Generative AI, Edge AI
๐น Interview Prep โ Leetcode for ML, System Design, ML Case Studies
๐น Portfolio Building โ GitHub, Kaggle Competitions, Writing Blogs
๐น Networking โ Contribute to open-source, Attend ML meetups, LinkedIn presence
Follow this advanced roadmap to build a successful career in ML!
The data field is vast, offering endless opportunities so start preparing now.
๐5โค2
๐ฑ ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฎ๐น๐น๐ฒ๐ป๐ด๐ฒ๐ ๐ง๐ต๐ฎ๐ ๐๐ฐ๐๐๐ฎ๐น๐น๐ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐๐ ๐ป
You donโt need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindsetโand these 5 types of challenges are the ones that actually matter.
Hereโs what to focus on (with examples) ๐
๐น 1. String Manipulation (Common in Data Cleaning)
โ Parse messy columns (e.g., split โName_Age_Cityโ)
โ Regex to extract phone numbers, emails, URLs
โ Remove stopwords or HTML tags in text data
Example: Clean up a scraped dataset from LinkedIn bias
๐น 2. GroupBy and Aggregation with Pandas
โ Group sales data by product/region
โ Calculate avg, sum, count using .groupby()
โ Handle missing values smartly
Example: โWhatโs the top-selling product in each region?โ
๐น 3. SQL Join + Window Functions
โ INNER JOIN, LEFT JOIN to merge tables
โ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
โ Use CTEs to break complex queries
Example: โGet 2nd highest salary in each departmentโ
๐น 4. Data Structures: Lists, Dicts, Sets in Python
โ Use dictionaries to map, filter, and count
โ Remove duplicates with sets
โ List comprehensions for clean solutions
Example: โCount frequency of hashtags in tweetsโ
๐น 5. Basic Algorithms (Not DP or Graphs)
โ Sliding window for moving averages
โ Two pointers for duplicate detection
โ Binary search in sorted arrays
Example: โDetect if a pair of values sum to 100โ
๐ฏ Tip: Practice challenges that feel like real-world data work, not textbook CS exams.
Use platforms like:
StrataScratch
Hackerrank (SQL + Python)
Kaggle Code
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
You donโt need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindsetโand these 5 types of challenges are the ones that actually matter.
Hereโs what to focus on (with examples) ๐
๐น 1. String Manipulation (Common in Data Cleaning)
โ Parse messy columns (e.g., split โName_Age_Cityโ)
โ Regex to extract phone numbers, emails, URLs
โ Remove stopwords or HTML tags in text data
Example: Clean up a scraped dataset from LinkedIn bias
๐น 2. GroupBy and Aggregation with Pandas
โ Group sales data by product/region
โ Calculate avg, sum, count using .groupby()
โ Handle missing values smartly
Example: โWhatโs the top-selling product in each region?โ
๐น 3. SQL Join + Window Functions
โ INNER JOIN, LEFT JOIN to merge tables
โ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
โ Use CTEs to break complex queries
Example: โGet 2nd highest salary in each departmentโ
๐น 4. Data Structures: Lists, Dicts, Sets in Python
โ Use dictionaries to map, filter, and count
โ Remove duplicates with sets
โ List comprehensions for clean solutions
Example: โCount frequency of hashtags in tweetsโ
๐น 5. Basic Algorithms (Not DP or Graphs)
โ Sliding window for moving averages
โ Two pointers for duplicate detection
โ Binary search in sorted arrays
Example: โDetect if a pair of values sum to 100โ
๐ฏ Tip: Practice challenges that feel like real-world data work, not textbook CS exams.
Use platforms like:
StrataScratch
Hackerrank (SQL + Python)
Kaggle Code
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
๐2โค1๐คฃ1
Seaborn Cheatsheet โ
โค6๐2
Data-Driven Decision Making
Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation.
1๏ธโฃ A/B Testing & Hypothesis Testing
A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better.
โ Key Metrics in A/B Testing:
Conversion Rate
Click-Through Rate (CTR)
Revenue per User
โ Steps in A/B Testing:
1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks").
2. Split users into Group A (control) and Group B (test).
3. Analyze differences using statistical tests.
โ SQL for A/B Testing:
Calculate average purchase per user in two test groups
Run a t-test to check statistical significance (Python)
๐น P-value < 0.05 โ Statistically significant difference.
๐น P-value > 0.05 โ No strong evidence of difference.
2๏ธโฃ Forecasting & Trend Analysis
Forecasting predicts future trends based on historical data.
โ Time Series Analysis Techniques:
Moving Averages (smooth trends)
Exponential Smoothing (weights recent data more)
ARIMA Models (AutoRegressive Integrated Moving Average)
โ SQL for Moving Averages:
7-day moving average of sales
โ Python for Forecasting (Using Prophet)
3๏ธโฃ KPI & Metrics Analysis
KPIs (Key Performance Indicators) measure business performance.
โ Common Business KPIs:
Revenue Growth Rate โ (Current Revenue - Previous Revenue) / Previous Revenue
Customer Retention Rate โ Customers at End / Customers at Start
Churn Rate โ % of customers lost over time
Net Promoter Score (NPS) โ Measures customer satisfaction
โ SQL for KPI Analysis:
Calculate Monthly Revenue Growth
โ Python for KPI Dashboard (Using Matplotlib)
4๏ธโฃ Real-Life Use Cases of Data-Driven Decisions
๐ E-commerce: Optimize pricing based on customer demand trends.
๐ Finance: Predict stock prices using time series forecasting.
๐ Marketing: Improve email campaign conversion rates with A/B testing.
๐ Healthcare: Identify disease patterns using predictive analytics.
Mini Task for You: Write an SQL query to calculate the customer churn rate for a subscription-based company.
Data Analyst Roadmap: ๐
https://t.iss.one/sqlspecialist/1159
Like this post if you want me to continue covering all the topics! โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation.
1๏ธโฃ A/B Testing & Hypothesis Testing
A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better.
โ Key Metrics in A/B Testing:
Conversion Rate
Click-Through Rate (CTR)
Revenue per User
โ Steps in A/B Testing:
1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks").
2. Split users into Group A (control) and Group B (test).
3. Analyze differences using statistical tests.
โ SQL for A/B Testing:
Calculate average purchase per user in two test groups
SELECT test_group, AVG(purchase_amount) AS avg_purchase
FROM ab_test_results
GROUP BY test_group;
Run a t-test to check statistical significance (Python)
from scipy.stats import ttest_ind
t_stat, p_value = ttest_ind(group_A['conversion_rate'], group_B['conversion_rate'])
print(f"T-statistic: {t_stat}, P-value: {p_value}")
๐น P-value < 0.05 โ Statistically significant difference.
๐น P-value > 0.05 โ No strong evidence of difference.
2๏ธโฃ Forecasting & Trend Analysis
Forecasting predicts future trends based on historical data.
โ Time Series Analysis Techniques:
Moving Averages (smooth trends)
Exponential Smoothing (weights recent data more)
ARIMA Models (AutoRegressive Integrated Moving Average)
โ SQL for Moving Averages:
7-day moving average of sales
SELECT order_date,
sales,
AVG(sales) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg
FROM sales_data;
โ Python for Forecasting (Using Prophet)
from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
model.plot(forecast)
3๏ธโฃ KPI & Metrics Analysis
KPIs (Key Performance Indicators) measure business performance.
โ Common Business KPIs:
Revenue Growth Rate โ (Current Revenue - Previous Revenue) / Previous Revenue
Customer Retention Rate โ Customers at End / Customers at Start
Churn Rate โ % of customers lost over time
Net Promoter Score (NPS) โ Measures customer satisfaction
โ SQL for KPI Analysis:
Calculate Monthly Revenue Growth
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,
(revenue - prev_month_revenue) / prev_month_revenue * 100 AS growth_rate
FROM revenue_data;
โ Python for KPI Dashboard (Using Matplotlib)
import matplotlib.pyplot as plt
plt.plot(df['month'], df['revenue_growth'], marker='o')
plt.title('Monthly Revenue Growth')
plt.xlabel('Month')
plt.ylabel('Growth Rate (%)')
plt.show()
4๏ธโฃ Real-Life Use Cases of Data-Driven Decisions
๐ E-commerce: Optimize pricing based on customer demand trends.
๐ Finance: Predict stock prices using time series forecasting.
๐ Marketing: Improve email campaign conversion rates with A/B testing.
๐ Healthcare: Identify disease patterns using predictive analytics.
Mini Task for You: Write an SQL query to calculate the customer churn rate for a subscription-based company.
Data Analyst Roadmap: ๐
https://t.iss.one/sqlspecialist/1159
Like this post if you want me to continue covering all the topics! โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐4โค3
Breaking into Data Science doesnโt need to be complicated.
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
๐ซ Spending months on theoretical concepts without hands-on practice.
๐ซ Overloading your resume with keywords instead of impactful projects.
๐ซ Believing you need a Ph.D. to break into the field.
Instead:
โ Start with Python or Rโfocus on mastering one language first.
โ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
โ Dive into a simple machine learning model (like linear regression) to understand the basics.
โ Solve real-world problems with open datasets and share them in a portfolio.
โ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
๐ซ Spending months on theoretical concepts without hands-on practice.
๐ซ Overloading your resume with keywords instead of impactful projects.
๐ซ Believing you need a Ph.D. to break into the field.
Instead:
โ Start with Python or Rโfocus on mastering one language first.
โ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
โ Dive into a simple machine learning model (like linear regression) to understand the basics.
โ Solve real-world problems with open datasets and share them in a portfolio.
โ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
โค6๐1๐ฅฐ1
An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.
Basically, there are 3 different layers in a neural network :
Input Layer (All the inputs are fed in the model through this layer)
Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
Output Layer (The data after processing is made available at the output layer)
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
Basically, there are 3 different layers in a neural network :
Input Layer (All the inputs are fed in the model through this layer)
Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
Output Layer (The data after processing is made available at the output layer)
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
๐2
How to master Python from scratch๐
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
๐5โค1
Essential Topics to Master Data Science Interviews: ๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes
Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
๐7โค1
Guys, Big Announcement!
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
โค4๐1