Shell

Data Science Analyst 

Amazon

Data Analyst

Data Science Analyst at Shell | Former Amazon Analyst | 2+ Years of Data-Driven Excellence

Welcome to my LinkedIn journey! 👋

I'm Sneha Vijaykumar, a passionate Data Scientist with a track record of making data speak, from the bustling world of Amazon to the dynamic landscape of Shell. With 2.4 years in the field, I've honed my skills in areas like Advanced Excel, SQL, Power BI, Tableau, Python, Machine Learning, Deep Learning, Natural Language Processing, and Azure.

But data science isn't just about the tech stack; it's about a holistic approach. My journey has shown me that:
🔍 Curiosity is the driving force behind discovering hidden insights.
🌟 Critical Thinking fuels the ability to tackle complex problems head-on.
🗣️ Communication bridges the gap between data and decisions.
🌐 Adaptability is essential in embracing the ever-evolving data landscape.
🤝 Teamwork is key to collaborating for solutions that make a real impact.
🔎 Attention to Detail is the precision that matters in data.

I'm here to connect with fellow data enthusiasts, industry professionals, and anyone interested in the fascinating world of data science. Let's explore the endless possibilities of data together! 

Excited to connect and learn together! See you on my profile. 😄

Here are a few Excel interview questions asked in interviews for a Analyst role👇

➡️Data Cleaning:

1)How would you handle missing values in a dataset?
2)How would you remove duplicate rows from a dataset?

➡️Formulas and Functions:

1)Can you explain the difference between VLOOKUP and HLOOKUP and when you might use each?
2)How would you use IF, AND, and OR functions to create a conditional formula?
3)How would you use the INDEX and MATCH functions together?

➡️Pivot Tables:

1)How would you create a pivot table to summarize data and find trends?
2)Can you explain how you would use pivot tables to analyze sales data (e.g., by region, product, or time period)?

➡️Data Visualization:

1)How would you create a chart or graph to visualize data trends?
2)How would you use conditional formatting to highlight important data points in a worksheet?

➡️Data Manipulation:

1)How would you filter data based on specific criteria?
2)How would you use the Text to Columns feature to split data in a single column into multiple columns?

➡️Advanced Excel Features:

1)Can you explain how to use the Data Validation feature to control data input?
2)How would you use Excel's What-If Analysis tools such as Goal Seek and Data Tables?

➡️Excel Scenarios:

1)How would you calculate the percentage change between two periods?
2)How would you create a dynamic dropdown list in Excel?
3)Can you walk me through how you would build a complex spreadsheet to analyze financial data?

These questions aim to assess  proficiency with Excel, problem-solving abilities, and  capacity to use Excel features to analyze data and generate insights.

#excel #interviewprep #dataanalytics #personalgrowth

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Sneha Vijaykumar

Here are a few basic SQL interview questions for a data analyst role

1)What is a Database?
2)What is DBMS?
3)What is RDBMS? And the difference between DBMS?
4)What is SQL?
5)What is the difference between SQL and MYSQL?
6)What are Tables & Fields?
7)Explain the different types of SQL commands?
8)Explain the difference between DROP, DELETE & TRUNCATE commands
9)What are the constraints in SQL? List down them & explain.
10)What is the difference between Primary Key & Unique constraint?
11)What is a query?
12)What are the common clauses written in a query?
13)What are aggregate & Scalar functions in SQL? Explain with examples
14)Explain Joins in SQL & it’s different types

Coding Questions:

1)Find the names of all employees whose name starts with 'J'.
2)Retrieve the top 5 highest earning employees.
3)Count the number of employees in each department.
4)Find the names of employees whose manager ID is not assigned (NULL).
5)Calculate the average salary of all employees

Will share next set of questions in my upcoming posts!!
Stay tuned for more...

#sql #interview #prep #dataanalytics

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Try solving these questions to test your basic SQL skills!

1)Selecting All Columns:
Write a query to select all columns from a table called employees.

2)Filtering Rows:
Write a query to select all columns from the employees table where the age is greater than 30.

3)Sorting Results:
Write a query to select all columns from the employees table and sort the results by the last_name column in ascending order.

4)Counting Rows:
Write a query to count the number of rows in the employees table.

5)Grouping and Aggregating:
Write a query to group the rows in the employees table by department and count the number of employees in each department.

6)Joining Tables:
Write a query to join the employees table with another table called departments on the department_id column. Display all columns from both tables.

7)Using Aliases:
Write a query to select the first_name and last_name columns from the employees table and combine them into a single column called full_name.

8)Inserting Data:
Write a query to insert a new row into the employees table with the values: employee_id = 101, first_name = 'John', last_name = 'Doe', age = 25, department_id = 1.

9)Updating Data:
Write a query to update the age column of an employee with employee_id = 101 in the employees table, setting it to 26.

10)Deleting Data:
Write a query to delete a row from the employees table where the employee_id is equal to 101.

#sql #dataanalytics #prep #personalgrowth

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Excel is a versatile tool for data analytics, and knowing the right functions can make our analysis more efficient and insightful. 

Here are some important Excel functions to learn for data science:

Lookup and Reference Functions:

➡️VLOOKUP(): Searches for a value in the first column of a range and returns a value from a specified column.
➡️HLOOKUP(): Similar to VLOOKUP(), but searches for a value in the first row and returns a value from a specified row.
➡️INDEX(): Returns the value of a cell in a specified row and column.
➡️MATCH(): Searches for a value in a range and returns its relative position.
➡️XLOOKUP(): An advanced lookup function that replaces VLOOKUP() and HLOOKUP(), offering more flexibility.

Statistical Functions:

➡️AVERAGE(): Calculates the mean of a range of values.
➡️MEDIAN(): Finds the middle value in a range.
➡️MODE(): Returns the most frequent value in a range.
➡️STDEV(), STDEV.P(), STDEV.S(): Calculate the standard deviation for a dataset (population or sample).
➡️VAR(), VAR.P(), VAR.S(): Calculate variance for a dataset (population or sample).

Mathematical Functions:

➡️SUM(): Adds up a range of values.
➡️PRODUCT(): Multiplies a range of values.
➡️ROUND(): Rounds a number to a specified number of decimal places.
➡️ABS(): Returns the absolute value of a number.

Logical Functions:

➡️IF(): Returns one value if a condition is true, and another value if false.
➡️AND(), OR(): Evaluate multiple conditions and return true or false.
➡️NOT(): Inverts a logical value (true becomes false, and vice versa).

Text Functions:

➡️CONCATENATE(): Combines two or more text strings into one.
➡️LEFT(), RIGHT(), MID(): Extract specific parts of a text string.
➡️LEN(): Returns the length of a text string.
➡️FIND(), SEARCH(): Locate the position of a substring within a text string.

Date and Time Functions:

➡️TODAY(): Returns the current date.
➡️NOW(): Returns the current date and time.
➡️DATE(): Creates a date from individual year, month, and day components.
➡️DATEDIF(): Calculates the difference between two dates.

PivotTables:

Not a function, but a powerful Excel tool for summarizing and analyzing large datasets by grouping and aggregating data in various ways.

Data Analysis ToolPak:

An Excel add-in that provides advanced statistical functions, including regression, ANOVA, t-tests, and more.

Learning these functions and tools can help us work more effectively with data in Excel and perform a wide range of data analysis tasks.

#excel #functions #dataanalytics #personalgrowth

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"May this Ugadi bring new beginnings, prosperity, and joy to you and your loved ones! Wishing you a year filled with success and happiness."

ಯುಗಾದಿ ಹಬ್ಬದ ಶುಭಾಷಯಗಳು! 🌟🎉

Python is a popular programming language for data analysis due to its simplicity, versatility, and the availability of numerous powerful libraries and tools. Here's an overview of how Python is used in data analysis:

➡ 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Python offers libraries like 𝐩𝐚𝐧𝐝𝐚𝐬 and 𝐧𝐮𝐦𝐩𝐲 that provide data structures (e.g., DataFrame) and functions for data manipulation, cleaning, and transformation. You can easily import data from various sources (CSV, Excel, SQL databases, etc.) and perform operations such as 𝐟𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠, 𝐚𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐫𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 data.

➡ 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Python provides libraries like 𝐦𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛 and 𝐬𝐞𝐚𝐛𝐨𝐫𝐧 for creating visualizations such as 𝐛𝐚𝐫 𝐜𝐡𝐚𝐫𝐭𝐬, 𝐥𝐢𝐧𝐞 𝐜𝐡𝐚𝐫𝐭𝐬, 𝐬𝐜𝐚𝐭𝐭𝐞𝐫 𝐩𝐥𝐨𝐭𝐬, 𝐡𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦𝐬, and more. These libraries allow you to present data in a clear and visually appealing manner.

➡ 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Libraries like scipy and statsmodels offer a range of statistical functions and models for data analysis. You can perform 𝐡𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬 𝐭𝐞𝐬𝐭𝐢𝐧𝐠, 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬, and other 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐭𝐞𝐬𝐭𝐬 to derive insights from data.

➡ 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Python's scikit-learn library is widely used for machine learning tasks such as 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧, 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧, 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠, and 𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧. It provides tools for data preprocessing, model selection, and model evaluation.

➡ 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Libraries like 𝐝𝐚𝐬𝐤 and 𝐦𝐨𝐝𝐢𝐧 allow you to process large datasets efficiently by parallelizing operations across multiple cores or machines.

➡ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐫𝐚𝐩𝐢𝐧𝐠: Python is commonly used for web scraping tasks using libraries such as 𝐫𝐞𝐪𝐮𝐞𝐬𝐭𝐬 and 𝐁𝐞𝐚𝐮𝐭𝐢𝐟𝐮𝐥𝐒𝐨𝐮𝐩 to gather data from websites.

➡ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠: You can use Python to generate reports automatically by pulling data from various sources, analyzing it, and creating visualizations and summaries.

➡ 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐎𝐭𝐡𝐞𝐫 𝐓𝐨𝐨𝐥𝐬: Python can easily integrate with other data-related technologies such as SQL databases, big data platforms (e.g., Hadoop, Spark), and cloud services.

#python #dataanalytics #personalgrowth #dataanalysis

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Here's an 𝐢𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞-𝐥𝐞𝐯𝐞𝐥 𝐒𝐐𝐋 question that requires 𝐥𝐨𝐠𝐢𝐜 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 and 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 skills. The question challenges your understanding of 𝐣𝐨𝐢𝐧𝐬, 𝐰𝐢𝐧𝐝𝐨𝐰 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬, and 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐥𝐨𝐠𝐢𝐜.

Given two tables 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 with column names '𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞_𝐢𝐝', '𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞_𝐧𝐚𝐦𝐞' & '𝐝𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭_𝐢𝐝' and 𝐬𝐚𝐥𝐚𝐫𝐢𝐞𝐬 with column names '𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞_𝐢𝐝', '𝐬𝐚𝐥𝐚𝐫𝐲_𝐝𝐚𝐭𝐞' & '𝐬𝐚𝐥𝐚𝐫𝐲_𝐚𝐦𝐨𝐮𝐧𝐭'. 

Write an SQL query to find the employees who have received a salary increase in three consecutive months.

Comment your answers below 👇 

#sql #datanalytics #interviewprep #dataanalysis #personalgrowth

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𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐂𝐡𝐚𝐫𝐭𝐬: 𝐀 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 

Data visualization is a powerful tool for 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 and 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐧𝐠 insights from data. Different types of charts serve different purposes. Let's explore some common types of charts and their applications:

1️⃣ 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 📊:
𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Comparing categorical data or showing changes over time.
𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Comparing values of different categories, such as sales by product category or revenue by month.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Bar chart comparing monthly sales for different products.

2️⃣ 𝐋𝐢𝐧𝐞 𝐂𝐡𝐚𝐫𝐭 📈:
𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Showing trends and changes over time.
𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing continuous data over a period, such as stock prices over months or temperature variations over days.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Line chart showing the trend of website traffic over a year.

3️⃣ 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭 🥧:
𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Displaying parts of a whole and illustrating proportions.
𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Showing the composition of a categorical variable, such as market share by product or distribution of expenses by category.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Pie chart illustrating the distribution of budget allocation for different departments.

4️⃣ 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 📊:
𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Representing the distribution of continuous data.
𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing the frequency distribution of numerical data, such as age distribution of survey respondents or distribution of exam scores.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Histogram showing the distribution of heights among a sample population.

5️⃣ 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭 📈:
𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Examining relationships between two continuous variables.
𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Identifying patterns and correlations between variables, such as the relationship between temperature and ice cream sales or the correlation between advertising spending and sales revenue.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Scatter plot depicting the relationship between hours studied and exam scores for students.

6️⃣ 𝐁𝐨𝐱 𝐏𝐥𝐨𝐭 📊:
𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Summarizing the distribution of numerical data and identifying outliers.
𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing the spread and skewness of data, comparing distributions, and identifying anomalies.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Box plot comparing the distribution of salaries for different job roles within a company.

7️⃣ 𝐇𝐞𝐚𝐭𝐦𝐚𝐩 🔥:
𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Displaying the magnitude of a variable in a matrix format.
𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing relationships and patterns in large datasets, such as correlation matrices or user engagement matrices.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Heatmap showing customer engagement levels across different demographics and products.

Remember to choose the appropriate chart type based on the nature of your data and the insights you want to convey.

#dataanalysis #visualization #charts #insights #analysis #eda

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𝐐𝐮𝐢𝐜𝐤 𝐒𝐐𝐋 𝐓𝐢𝐩 👇 

𝐔𝐬𝐞 𝐋𝐈𝐌𝐈𝐓 𝐭𝐨 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞 𝐚 𝐒𝐮𝐛𝐬𝐞𝐭 𝐨𝐟 𝐑𝐨𝐰𝐬:

When you're working with large data and you only need to retrieve a small subset of rows, use the 𝐋𝐈𝐌𝐈𝐓 𝐜𝐥𝐚𝐮𝐬𝐞 to limit the number of rows returned by your query. This can improve 𝐪𝐮𝐞𝐫𝐲 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 and 𝐫𝐞𝐝𝐮𝐜𝐞 𝐭𝐡𝐞 𝐚𝐦𝐨𝐮𝐧𝐭 𝐨𝐟 𝐝𝐚𝐭𝐚 transferred over the network.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞:
𝘚𝘌𝘓𝘌𝘊𝘛 * 𝘍𝘙𝘖𝘔 𝘰𝘳𝘥𝘦𝘳𝘴 𝘓𝘐𝘔𝘐𝘛 10;

This query will return only the 𝐟𝐢𝐫𝐬𝐭 10 𝐫𝐨𝐰𝐬 from the "orders" table, which can be helpful for initial data exploration or testing queries on a subset of data.

#sql #datanalytics #personalgrowth #tipsandtricks

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Are you gearing up for an upcoming interview? Prepared for the technical rounds? What about your other 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐬𝐨𝐟𝐭 𝐬𝐤𝐢𝐥𝐥𝐬 crucial for data science roles, which are 𝐞𝐪𝐮𝐚𝐥𝐥𝐲 𝐚𝐬𝐬𝐞𝐬𝐬𝐞𝐝? 

Don't fret—I've got you covered. 

Here's a compilation of questions designed to evaluate 𝐩𝐫𝐨𝐛𝐥𝐞𝐦-𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐩𝐫𝐨𝐰𝐞𝐬𝐬, 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐚𝐜𝐮𝐦𝐞𝐧, 𝐚𝐧𝐝 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 in data science interviews.

1)Describe a complex data science project you've worked on. How did you approach the problem, and what were the key challenges you faced?

2)Given a dataset with missing values, how would you handle them? What are some common imputation techniques you could use?

3)You're given a dataset with millions of records. How would you approach data preprocessing to prepare it for analysis efficiently?

4)Describe a situation where you encountered unexpected results in your analysis. How did you troubleshoot the issue and find a solution?

5)Given a business problem, how would you formulate it as a data science problem? What factors would you consider in defining the problem and selecting appropriate techniques?

6)Explain the bias-variance tradeoff and how it relates to model performance. How would you address high bias or high variance in a machine learning model?

7)Imagine you're tasked with building a recommendation system for an e-commerce platform. How would you approach designing and implementing the recommendation algorithm?

8)You're given a time-series dataset. How would you analyze it to identify trends, seasonality, and anomalies? What techniques or algorithms would you apply?

9)Describe a situation where you had to prioritize multiple competing tasks or projects. How did you manage your time and resources to ensure successful completion?

10)Given a large-scale dataset, how would you assess and improve the performance of a machine learning model? What strategies would you employ for feature selection, model tuning, and evaluation?

11)Explain the concept of overfitting in machine learning. How can you prevent overfitting and ensure the generalization of your model?

12)Describe a scenario where you had to communicate technical findings or insights to non-technical stakeholders. How did you tailor your message to ensure understanding and buy-in?

13)You're given a messy, unstructured dataset. How would you approach data cleaning and transformation to extract meaningful insights?

14)Imagine you're presented with a business problem that lacks sufficient data. How would you address data scarcity or quality issues to still provide valuable insights or recommendations?

15)Describe a time when you had to learn a new tool, technology, or programming language to solve a problem. How did you approach the learning process, and what was the outcome?

Keep sharpening those problem-solving skills and stay curious! 

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𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 plays a crucial role in data science, providing the 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 for understanding and implementing various algorithms and techniques. Here's a list of key mathematics concepts required for data science:

1)𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚:
➡ Vectors and Matrices
➡ Matrix Operations (Addition, Multiplication, Inverse)
➡ Eigenvalues and Eigenvectors
➡ Singular Value Decomposition (SVD)
➡ Principal Component Analysis (PCA)

2) 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬:
➡ Differentiation and Integration
➡ Partial Derivatives
➡ Gradient Descent
➡ Optimization Techniques

3)𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐓𝐡𝐞𝐨𝐫𝐲:
➡ Probability Distributions (e.g., Normal, Poisson, Binomial)
➡ Bayes' Theorem
➡ Conditional Probability
➡ Expectation and Variance
➡ Central Limit Theorem

4)𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬:
➡ Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
➡ Inferential Statistics (Hypothesis Testing, Confidence Intervals)
➡ Regression Analysis (Linear Regression, Logistic Regression)
➡ Probability Distributions (Continuous and Discrete)
➡ Statistical Testing (t-tests, ANOVA, Chi-Square)

5) 𝐌𝐮𝐥𝐭𝐢𝐯𝐚𝐫𝐢𝐚𝐛𝐥𝐞 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬:
➡ Gradients and Hessians
➡ Partial Derivatives of Multivariable Functions
➡ Multivariable Optimization

6) 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
➡ Convex Optimization
➡ Gradient Descent Variants (Stochastic Gradient Descent, Mini-batch Gradient Descent)
➡ Newton's Method

7) 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐡𝐞𝐨𝐫𝐲:
➡ Entropy and Information Gain
➡ Kullback-Leibler Divergence
➡ Mutual Information

8) 𝐆𝐫𝐚𝐩𝐡 𝐓𝐡𝐞𝐨𝐫𝐲:
➡ Graph Representation
➡ Shortest Path Algorithms (Dijkstra's Algorithm)
➡ PageRank Algorithm

9) 𝐃𝐢𝐬𝐜𝐫𝐞𝐭𝐞 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬:
➡ Combinatorics
➡ Set Theory
➡ Propositional and Predicate Logic

10) 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
➡ Autocorrelation and Cross-correlation
➡ Fourier Transform
➡ ARIMA Models
➡ Seasonality and Trends

Understanding these 𝐦𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 is essential for grasping the underlying principles of algorithms and techniques used in data science, which enables data scientists to 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐚𝐧𝐚𝐥𝐲𝐳𝐞 and 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐝𝐚𝐭𝐚, build predictive models, and derive meaningful insights.

#mathematics #datascience #foundation #concepts 

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Here are a few Python basic interview questions for a data science role:

List Comprehensions:

1)What is a list comprehension in Python, and how does it work?
2)Provide an example of using a list comprehension to create a list of squared numbers from 1 to 10.

String Manipulation:

1)How would you concatenate two strings in Python?
2)Explain the difference between the "split()" and "join()" methods in Python strings.

File Handling:

1)How would you open and read a text file in Python?
2)Explain the purpose of the "with" statement when working with files in Python.

Dictionary Manipulation:

1)How do you access a value associated with a specific key in a dictionary?
2)Explain the purpose of the "items()" method in Python dictionaries

Object-Oriented Programming (OOP):

1)What is a class in Python, and how do you define one?
2)Provide an example of creating a class with a constructor (init) and a method in Python.

Error Handling:

1)How would you raise a custom exception in Python?
2)Explain the purpose of the "assert" statement in Python error handling.

Modules and Packages:

1)What is a Python module, and how do you import it into your code?
2)Explain the difference between a module and a package in Python.

Scope and Lifetime of Variables:

1)What is the scope of a variable in Python?
2)Explain the difference between local, global, and nonlocal variables in Python.

These questions cover fundamental concepts in Python programming like syntax, data manipulation that are important for data science roles. 

#python #datascience #interview #prep

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Few of the amazing 𝐟𝐫𝐞𝐞 𝐦𝐨𝐜𝐤 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐬𝐢𝐭𝐞𝐬 that I used during my preparation 👇 

1) pramp.com
2) interviewing.io
3) prepbunk.com
4) interviewbit.com ( My personal favorite site)
5) remasto.com

Let me know in comments if you have used any of these sites before..

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#mock #interviews #prep #free #resources

𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬 are computational algorithms that learn 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 and 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩𝐬 from data to make 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐬 or 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 without being explicitly programmed. 

Here's a brief introduction to some common machine learning models:

➡ 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧: A simple linear model used for regression tasks.
➡ 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧: Used for binary classification problems.
➡ 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐞𝐞𝐬: Non-linear models that partition the feature space into regions.
➡ 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭𝐬: Ensembles of decision trees that improve performance and robustness.
➡ 𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐆𝐁𝐌): Sequentially trains multiple weak learners to correct errors of the previous models.
➡ 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌): Effective for both linear and non-linear classification tasks by finding the optimal hyperplane.
➡ 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍): A non-parametric method used for classification and regression tasks.
➡ 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐍𝐍): Deep learning models consisting of interconnected nodes inspired by the human brain.
➡ 𝐂𝐨𝐧𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐂𝐍𝐍): Specialized neural networks for image recognition tasks.
➡ 𝐑𝐞𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐑𝐍𝐍): Designed to work with sequential data, such as time series or natural language.
➡ 𝐋𝐨𝐧𝐠 𝐒𝐡𝐨𝐫𝐭-𝐓𝐞𝐫𝐦 𝐌𝐞𝐦𝐨𝐫𝐲 (𝐋𝐒𝐓𝐌): A type of RNN architecture that mitigates the vanishing gradient problem.
➡ 𝐆𝐚𝐭𝐞𝐝 𝐑𝐞𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐔𝐧𝐢𝐭 (𝐆𝐑𝐔): Another type of RNN architecture, similar to LSTM but with fewer parameters.
➡ 𝐀𝐮𝐭𝐨𝐞𝐧𝐜𝐨𝐝𝐞𝐫𝐬: Neural networks used for unsupervised learning tasks, typically for dimensionality reduction or feature learning.
➡ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐆𝐀𝐍): Comprising a generator and a discriminator, used for generating synthetic data.
➡ 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐏𝐂𝐀): A linear dimensionality reduction technique.
➡ 𝐊-𝐌𝐞𝐚𝐧𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠: An unsupervised clustering algorithm used to partition data into clusters.
➡ 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠: A clustering algorithm that builds a tree of clusters.
➡ 𝐍𝐚𝐢𝐯𝐞 𝐁𝐚𝐲𝐞𝐬: A simple probabilistic classifier based on applying Bayes' theorem.
➡ 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐆𝐌𝐌): A probabilistic model that assumes all data points are generated from a mixture of several Gaussian distributions.
➡ 𝐇𝐢𝐝𝐝𝐞𝐧 𝐌𝐚𝐫𝐤𝐨𝐯 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐇𝐌𝐌): Used for modeling sequential data with underlying hidden states.

Feel free to add in comments if I have missed any!!

#machinelearning #datascience #models #personalgrowth

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✨ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐒𝐞𝐫𝐢𝐞𝐬 ✨ 

𝑷𝒚𝒕𝒉𝒐𝒏 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆 - 1

Test your Python skills with my interview series, starting with this challenging question

#python #interview #prep #challenge

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Are you finding yourself repeatedly writing the same code for different parts of your project? If so, you might be missing out on a powerful programming paradigm that can revolutionize your workflow: 𝐎𝐛𝐣𝐞𝐜𝐭-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 (𝐎𝐎𝐏).

OOP isn't just another coding technique; it's a mindset that can streamline your development process and make your codebase more robust and maintainable. 
At its core, OOP is all about 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐜𝐨𝐝𝐞 into reusable building blocks called 𝐨𝐛𝐣𝐞𝐜𝐭𝐬.

Imagine you're working on a data analysis project. Without OOP, you might find yourself duplicating code to handle similar tasks like data preprocessing, analysis, and visualization. This duplication not only wastes time but also increases the chances of 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐛𝐮𝐠𝐬 and 𝐢𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐢𝐞𝐬 into your code.

With OOP, you can encapsulate common functionality into reusable 𝐨𝐛𝐣𝐞𝐜𝐭𝐬. 
But OOP doesn't stop there. It also introduces concepts like 𝐢𝐧𝐡𝐞𝐫𝐢𝐭𝐚𝐧𝐜𝐞 and 𝐩𝐨𝐥𝐲𝐦𝐨𝐫𝐩𝐡𝐢𝐬𝐦, which further enhance code reusability. 

So, if you often find yourself reinventing the wheel in your code, it's time to explore the world of 𝐎𝐛𝐣𝐞𝐜𝐭-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠.

Follow me for more... 😊 
#python #oop #coding #standards

𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐌𝐚𝐝𝐞 𝐒𝐢𝐦𝐩𝐥𝐞: 𝐓𝐡𝐞 𝐅𝐢𝐬𝐡𝐛𝐨𝐧𝐞 𝐃𝐢𝐚𝐠𝐫𝐚𝐦 🐟

Got a problem that's got you scratching your head? Meet the f𝐢𝐬𝐡𝐛𝐨𝐧𝐞 𝐝𝐢𝐚𝐠𝐫𝐚𝐦 also known as the 𝐈𝐬𝐡𝐢𝐤𝐚𝐰𝐚 𝐝𝐢𝐚𝐠𝐫𝐚𝐦 or 𝐜𝐚𝐮𝐬𝐞-𝐚𝐧𝐝-𝐞𝐟𝐟𝐞𝐜𝐭 𝐝𝐢𝐚𝐠𝐫𝐚𝐦, a visual tool used for 𝐩𝐫𝐨𝐛𝐥𝐞𝐦-𝐬𝐨𝐥𝐯𝐢𝐧𝐠 and 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐨𝐨𝐭 𝐜𝐚𝐮𝐬𝐞𝐬 of a particular issue

🔹 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐢𝐭? The fishbone diagram helps you break down a problem by listing its possible causes, making it easier to find the real source of the trouble.

🔹 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐰𝐨𝐫𝐤? Start by stating the problem, then list categories like People, Process, Equipment, Environment, and Materials. Under each category, jot down what might be causing the issue.

🔹 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫? It helps you see the big picture and tackle the root cause, not just the symptoms, leading to better solutions.

🔹 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: It's a team-friendly way to solve problems, encourages everyone to pitch in ideas, and gets you closer to fixing things faster.

🔹 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Next time you're stuck, give the fishbone diagram a try. It's like a roadmap to solving problems! 

PS: I've found it incredibly useful in my own 𝐩𝐫𝐨𝐛𝐥𝐞𝐦-𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐣𝐨𝐮𝐫𝐧𝐞𝐲.

 #ProblemSolving #FishboneDiagram #RootCause #SimpleSolutions

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In the world of data science, 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 and 𝐫𝐨𝐛𝐮𝐬𝐭 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 are key to success. One of the best tools we have at our disposal to achieve these goals is 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. Here's why tools like 𝐆𝐢𝐭 are essential for projects:

1)𝐂𝐡𝐚𝐧𝐠𝐞 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠: Version control tools track changes made to files, such as code, data, and documentation. They record who made the changes and when, along with descriptions of the changes.
2)𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Multiple users can work on the same project simultaneously, making changes to different files or even the same file. Version control tools help manage these changes and resolve conflicts.
3)𝐁𝐫𝐚𝐧𝐜𝐡𝐢𝐧𝐠 𝐚𝐧𝐝 𝐌𝐞𝐫𝐠𝐢𝐧𝐠: Branching allows users to create separate lines of development within a project, enabling experimentation without affecting the main codebase. Merging lets users combine changes from different branches back into the main branch.
4)𝐂𝐨𝐦𝐦𝐢𝐭 𝐇𝐢𝐬𝐭𝐨𝐫𝐲: The commit history shows a log of all changes made to the project, including details such as commit messages, timestamps, and authors. This history helps trace the evolution of the project.
5)𝐁𝐚𝐜𝐤𝐮𝐩 𝐚𝐧𝐝 𝐑𝐞𝐝𝐮𝐧𝐝𝐚𝐧𝐜𝐲: By storing the project in a version control system, users have a backup of their work, providing redundancy in case of data loss.
6)𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐂𝐨𝐝𝐞 𝐑𝐞𝐯𝐢𝐞𝐰: Version control tools facilitate collaboration by allowing users to review each other's work and provide feedback.

𝐒𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐭𝐨𝐨𝐥𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:
1) Git
2) CVS
3) Subversion(SVS)
4) Mercurial

#versioncontrol #datascience #git #Projectmanagement #personalgrowth


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