Practical interview question for an entry-level data analyst role in #Power_BI: along with answers !

Question: Data Modeling Case: You have sales data and customer data in separate tables. How would you model this data in Power BI to analyze customer purchase behavior?

👉 Load the Data: Import the sales data and customer data tables into Power BI.

👉 Establish Relationships: Identify the CustomerID as the common key between the two tables. In the "Model" view, create a relationship by connecting the CustomerID column from the Sales Data table to the CustomerID column in the Customer Data table.

👉 Data Structure:

Sales Data Table: Contains columns like SaleID, CustomerID, ProductID, SaleDate, and Amount.

Customer Data Table: Contains columns like CustomerID, CustomerName, Age, Gender, and Location.

👉 Create Visualizations:

Total Sales by Customer: A bar chart showing the total amount spent by each customer.

Sales Over Time: A line chart displaying sales trends over time for each customer.

Customer Demographics: Pie charts or bar charts illustrating sales distribution by customer age, gender, and location.

👉 Utilize DAX for Advanced Analysis: Create measures using DAX (Data Analysis Expressions) to calculate total sales and sales by specific customer attributes for deeper insights.

By following these steps, you can effectively model your data in Power BI to gain meaningful insights into customer purchase behavior.

In Tableau, Level of Detail (LOD) expressions allow you to control the granularity of aggregations. Power BI has similar functionalities through the use of DAX (Data Analysis Expressions). While Power BI doesn't have direct LOD expressions, you can achieve the same results using DAX functions. Here are some common scenarios and their DAX equivalents:

1. Fixed LOD Expressions:
In Tableau, a fixed LOD expression computes values at a specific granularity, independent of the visualization's granularity.

Tableau:

   { FIXED [Dimension1], [Dimension2]: SUM([Measure]) }
   

Power BI (DAX):

   CALCULATE(SUM('Table'[Measure]), ALLEXCEPT('Table', 'Table'[Dimension1], 'Table'[Dimension2]))
   

2. Include LOD Expressions:
An include LOD expression adds a specific dimension to the granularity of the existing view.

Tableau:

   { INCLUDE [Dimension]: SUM([Measure]) }
   

Power BI (DAX):

   CALCULATE(SUM('Table'[Measure]), ALL('Table'[Dimension]))
   

3. Exclude LOD Expressions:
An exclude LOD expression removes a specific dimension from the granularity of the existing view.

Tableau:

   { EXCLUDE [Dimension]: SUM([Measure]) }
   

Power BI (DAX):

   CALCULATE(SUM('Table'[Measure]), REMOVEFILTERS('Table'[Dimension]))
   

4. Row-level Calculations:
To perform calculations at the row level and then aggregate the result, you can use the SUMX function in DAX.

Tableau:

   SUM([Measure1] + [Measure2])
   

Power BI (DAX):

   SUMX('Table', 'Table'[Measure1] + 'Table'[Measure2])
   

These DAX functions allow you to achieve similar results as Tableau's LOD expressions by giving you control over the context and granularity of calculations.

Frontend: HTML, CSS, JavaScript, React

Backend: Python, Node.Js, Ruby, Java, API

Database: Mysql, MongoDB, Oracle, PostgreSQL

Cloud: AWS, Azure, Google Cloud, Digital Ocean

UI/UX: Figma, Adobe XD, Visual Studio, Git

*You can learn ReactJS easily 🤩*

Here's all you need to get started 🙌

1.Components
• Functional Components
• Class Components
• JSX (JavaScript XML) Syntax

2.Props (Properties)
• Passing Props
• Default Props
• Prop Types

3.State
• useState Hook
• Class Component State
• Immutable State

4.Lifecycle Methods (Class Components)
• componentDidMount
• componentDidUpdate
• componentWillUnmount

5.Hooks (Functional Components)
• useState
• useEffect
• useContext
• useReducer
• useCallback
• useMemo
• useRef
• useImperativeHandle
• useLayoutEffect

6.Event Handling
• Handling Events in Functional Components
• Handling Events in Class Components

7.Conditional Rendering
• if Statements
• Ternary Operators
• Logical && Operator

8.Lists and Keys
• Rendering Lists
• Keys in React Lists

9.Component Composition
• Reusing Components
• Children Props
• Composition vs Inheritance

10.Higher-Order Components (HOC)
• Creating HOCs
• Using HOCs for Reusability

11.Render Props
• Using Render Props Pattern

12.React Router
• <BrowserRouter>
• <Route>
• <Link>
• <Switch>
• Route Parameters

13.Navigation
• useHistory Hook
• useLocation Hook

State Management

14.Context API
• Creating Context
• useContext Hook

15.Redux
• Actions
• Reducers
• Store
• connect Function (React-Redux)

16.Forms
• Handling Form Data
• Controlled Components
• Uncontrolled Components

17.Side Effects
• useEffect for Data Fetching
• useEffect Cleanup

18.AJAX Requests
• Fetch API
• Axios Library

Error Handling

19.Error Boundaries
• componentDidCatch (Class Components)
• ErrorBoundary Component (Functional
Components)

20.Testing
• Jest Testing Framework
• React Testing Library

21. Best Practices
• Code Splitting
• PureComponent and React.memo
• Avoiding Reconciliation
• Keys for Dynamic Lists

22.Optimization
• Memoization
• Profiling and Performance Monitoring

23. Build and Deployment
• Create React App (CRA)
• Production Builds
• Deployment Strategies

Frameworks and Libraries

24.Styling Libraries
• Styled-components
• CSS Modules

25.State Management Libraries
• Redux
• MobX

26.Routing Libraries
• React Router
• Reach Router

Basic web development roadmap
🟧
1.Learn: How websites work, front-end vs back-end, code editor‌‌
🟧
2: Basic front-end:
a. Html
b. Css
c. Javascript
Expected time 7+14+30=51 days.
🟧
3. Learn front-end frameworks:
a. Learn css framework ( Bootstrap , Tailwind css , ...)
b. Learn JavaScript frameworks ( angular, react , vue...)

Expected time minimum 60 days.
🟧
4. Learn database
a. MySQL
b. MongoDB
There are many more. Choose one and learn.
🟧
5. Learn backend programming languages:
a. Php
b. Nodejs
There are many more. Learn any one.

Expected time: 60 days
🟧
6. Do some projects and clone some websites.
▫️▫️🟧▫️▫️

Here's a short roadmap to crack an IT job with a non-CS background 🚀

1. 📚 Learn basics of CS and programming.
2. 🎯 Choose a specialization (e.g., web dev, data analysis).
3. 🏆 Complete online courses and certifications.
4. 🛠️ Build a portfolio of projects.
5. 🤝 Network with professionals.
6. 💼 Seek internships for experience.
7. 📚 Keep learning and stay updated.
8. 🧠 Develop soft skills.
9. 📝 Prepare for interviews.
10. 💪 Stay persistent and positive! Good luck!

𝗚𝗶𝘁 𝘃𝘀 𝗚𝗶𝘁𝗛𝘂𝗯: What's the Difference?

Ever mixed up Git and GitHub? You’re not alone—they’re related but serve distinct purposes!

𝐆𝐢𝐭: A powerful version control system that tracks changes in your code. It’s your local toolkit for managing versions, rolling back changes, and collaborating.

𝐆𝐢𝐭𝐇𝐮𝐛: A cloud-based platform that hosts Git repositories online. It enhances collaboration by letting you share, review, and manage code—think of it as a social network for developers.

In short:
Git = Local version control tool
GitHub = Cloud-based hosting service for Git repositories

Understanding the difference can significantly improve your workflow and collaboration in software development!

What Is MERN?

MERN Stack is a Javascript Stack that is used for easier and faster deployment of full-stack web applications. MERN Stack comprises of 4 technologies namely: MongoDB, Express, React and Node.js. It is designed to make the development process smoother and easier.

MongoDB

MongoDb is a NoSQL DBMS where data is stored in the form of documents having key-value pairs similar to JSON objects. MongoDB enables users to create databases, schemas and tables.

ExpressJS

ExpressJS is a NodeJS framework that simplifies writing the backend code. It saves you from creating multiple Node modules.



ReactJS

ReactJS is a JS library that allows the development of user interfaces for mobile apps and SPAs. It allows you to code Javascript and develop UI components.

NodeJS

NodeJS is an open-source Javascript runtime environment that allows users to run code on the server.

©How fresher can get a job as a data scientist?©

Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?

The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.

Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.

All the major data science jobs for freshers will only be available through off-campus interviews.

Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner

Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.

Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

How Git Works - From Working Directory to Remote Repository

[1]. Working Directory:
Your project starts here. The working directory is where you actively make changes to your files.
[2]. Staging Area (Index):
After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint.
[3]. Local Repository:
Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points.
[4]. Stash (Optional):
If needed, use git stash to temporarily save changes without committing. Useful when switching branches or performing other tasks.
[5]. Remote Repository:
The remote repository, hosted on platforms like GitHub, is a version of your project accessible to others. Use git push to send local commits and git pull to fetch remote changes.
[6]. Remote Branch Tracking:
Local branches can be set to track corresponding branches on the remote. This eases synchronization with git pull or git push.