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How To Code in Python 3
by Lisa Tagliaferri


📄 459 pages

🔗 Book link

Dear Data Fam,

If you are looking to kick start Azure Data Engineering from Starch , check out this document !!

It will help you to understand a basic end to end prod flow

Hey guys,

Today, I curated a list of essential Power BI interview questions that every aspiring data analyst should be prepared to answer 👇👇

1. What is Power BI?

Power BI is a business analytics service developed by Microsoft. It provides tools for aggregating, analyzing, visualizing, and sharing data. With Power BI, users can create dynamic dashboards and interactive reports from multiple data sources.

Key Features:
- Data transformation using Power Query
- Powerful visualizations and reporting tools
- DAX (Data Analysis Expressions) for complex calculations

2. What are the building blocks of Power BI?

The main building blocks of Power BI include:
- Visualizations: Graphical representations of data (charts, graphs, etc.).
- Datasets: A collection of data used to create visualizations.
- Reports: A collection of visualizations on one or more pages.
- Dashboards: A single page that combines multiple visualizations from reports.
- Tiles: Single visualization found on a report or dashboard.

3. What is DAX, and why is it important in Power BI?

DAX (Data Analysis Expressions) is a formula language used in Power BI for creating custom calculations and aggregations. DAX is similar to Excel formulas but offers much more powerful data manipulation capabilities.

Tip: Be ready to explain not just the syntax, but scenarios where DAX is essential, such as calculating year-over-year growth or creating dynamic measures.

4. How does Power BI differ from Excel in data visualization?

While Excel is great for individual analysis and data manipulation, Power BI excels in handling large datasets, creating interactive dashboards, and sharing insights across the organization. Power BI also integrates better and allows for real-time data streaming.

5. What are the types of filters in Power BI, and how are they used?

Power BI offers several types of filters to refine data and display only what’s relevant:

- Visual-level filters: Apply filters to individual visuals.
- Page-level filters: Apply filters to all the visuals on a report page.
- Report-level filters: Apply filters to all pages in the report.

Filters help to create more customized and targeted reports by narrowing down the data view based on specific conditions.

6. What are Power BI Desktop, Power BI Service, and Power BI Mobile? How do they interact?

- Power BI Desktop: A desktop-based application used for data modeling, creating reports, and building dashboards.
- Power BI Service: A cloud-based platform that allows users to publish and share reports created in Power BI Desktop.
- Power BI Mobile: Allows users to view reports and dashboards on mobile devices for on-the-go access.

These components work together in a typical workflow:
1. Build reports and dashboards in Power BI Desktop.
2. Publish them to the Power BI Service for sharing and collaboration.
3. View and interact with reports on Power BI Mobile for easy access anywhere.

7. Explain the difference between calculated columns and measures.

- Calculated columns are added to a table using DAX and are calculated row by row.
- Measures are calculations used in aggregations, such as sums, averages, and ratios. Unlike calculated columns, measures are dynamic and evaluated based on the filter context of a report.

8. How would you perform data cleaning and transformation in Power BI?

Data cleaning and transformation in Power BI are mainly done using Power Query Editor. Here, you can:
- Remove duplicates or empty rows
- Split columns (e.g., text into multiple parts)
- Change data types (e.g., text to numbers)
- Merge and append queries from different data sources

Power BI isn’t just about visuals; it’s about turning raw data into actionable insights. So, keep honing your skills, try building dashboards, and soon enough, you’ll be impressing your interviewers too!

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.me/DataSimplifier

Share with credits: https://t.me/sqlspecialist

Hope it helps :)

Hello Guys,

Please check this document On resolving frequent issues we see in Azure data factory development s.

🚀 Key Skills for Aspiring Tech Specialists

📊 Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques

🧠 Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks

🏗 Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools

🤖 Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus

🧠 Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning

🤯 AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills

🔊 NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data

🌟 Embrace the world of data and AI, and become the architect of tomorrow's technology!

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://t.me/datasciencefun

ENJOY LEARNING 👍👍

Python Detailed Roadmap 🚀

📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)

📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules

📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON

📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation

📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions

📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators

📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)

📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)

📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI

📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)

📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub

📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews

Like for more ❤️💪

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 :)

Essential Data Science Concepts Everyone Should Know:

1. Data Types and Structures:

• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)

• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)

• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)

2. Descriptive Statistics:

• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)

• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)

• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)

3. Probability and Statistics:

• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)

• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)

• Confidence Intervals: Estimating the range of plausible values for a population parameter

4. Machine Learning:

• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)

• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)

• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)

5. Data Cleaning and Preprocessing:

• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)

• Outlier Detection and Removal: Identifying and addressing extreme values

• Feature Engineering: Creating new features from existing ones (e.g., combining variables)

6. Data Visualization:

• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)

• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)

7. Ethical Considerations in Data Science:

• Data Privacy and Security: Protecting sensitive information

• Bias and Fairness: Ensuring algorithms are unbiased and fair

8. Programming Languages and Tools:

• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn

• R: Statistical programming language with strong visualization capabilities

• SQL: For querying and manipulating data in databases

9. Big Data and Cloud Computing:

• Hadoop and Spark: Frameworks for processing massive datasets

• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)

10. Domain Expertise:

• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis

• Problem Framing: Defining the right questions and objectives for data-driven decision making

Bonus:

• Data Storytelling: Communicating insights and findings in a clear and engaging manner

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍