Data Scientist — Data Science Interview Prep

Building Blocks - Description: Focus on the foundational mathematical concepts that underpin data science. We'll concentrate on topics critical to understanding the underlying principles without getting bogged down in complex calculations. - Specific Resources/Activities: - Watch videos or read articles on: - Basic algebra (variables, equations, inequalities) - Basic statistics (mean, median, mode, standard deviation, variance) - Probability (basic probability calculations, understanding distributions like the normal distribution - visually) - Complete some practice problems related to the above topics (e.g., Khan Academy provides free math courses). - Expected Outcomes: A basic understanding of key mathematical concepts related to data analysis and problem-solving, and the ability to solve simple exercises.

The Basics - Description: Start learning the Python programming language, a core skill for data scientists. Focus on fundamental Python syntax, data types, and basic operations. Don’t get caught up in code, focus on the logic. - Specific Resources/Activities: - Follow an introductory Python tutorial (e.g., Codecademy's "Learn Python 3" or similar, or freeCodeCamp Python courses). - Learn about: - Data types (integers, floats, strings, booleans, lists, dictionaries) - Basic operators (+, -, , /, //, %, ==, !=, >, <, >=, <=) - Control flow (if/else statements, for loops, while loops) - Practice writing simple Python scripts that perform basic calculations, manipulate strings, and work with lists/dictionaries. - Expected Outcomes:* Knowledge of basic Python syntax, data types, and control flow; the ability to write and run simple Python scripts.

Your Data Toolkit - Description: Introduce the Pandas library, a critical tool for data manipulation and analysis in Python. Focus on essential Pandas operations. - Specific Resources/Activities: - Go through a Pandas tutorial (e.g., Pandas documentation tutorials, DataCamp's "Introduction to Python for Data Science"). - Learn how to: - Create and access data in Pandas DataFrames. - Load data from different file formats (CSV). - Filter and sort data. - Handle missing values (e.g., using fillna()). - Perform basic data transformations. - Practice manipulating a sample dataset in Pandas. - Expected Outcomes: Familiarity with Pandas DataFrames; the ability to load, explore, clean, and manipulate data using Pandas.

Telling the Story - Description: Introduce Matplotlib and Seaborn, libraries that help you visualize data, so you can tell stories through your data and present your findings effectively. - Specific Resources/Activities: - Learn about Matplotlib and Seaborn by watching tutorials or going through a beginner-friendly course. - Learn how to create: - Line plots, bar plots, scatter plots, histograms, and box plots. - Customize plots with titles, labels, and legends. - Create basic visualizations to explore a sample dataset. - Expected Outcomes: The ability to create basic data visualizations using Matplotlib and Seaborn to gain insights from data and communicate your findings visually.

First Steps - Description: Introduce the core concept of machine learning in a very conceptual way. Begin discussing key machine learning algorithms. Also, start thinking about how to frame your answers during an interview. - Specific Resources/Activities: - Watch videos or read articles about what machine learning is and how it works (e.g., Khan Academy's introduction to machine learning). - Learn about the difference between supervised, unsupervised, and reinforcement learning. - Briefly explain common algorithms like linear regression and k-means clustering. - Start practicing answering common interview questions, such as "What is machine learning?" or "Explain the difference between classification and regression". - Expected Outcomes: A general understanding of what machine learning is; familiarity with basic algorithms and their use cases; the ability to answer basic interview questions in your own words.

Preparing for the Journey - Description: Consolidate your knowledge and prepare for future learning. Review all previous topics and start practicing coding or interview exercises. - Specific Resources/Activities: - Review all the concepts learned during the week. - Work through a few practice interview questions related to the topics covered. - Solve some coding exercises on platforms like HackerRank or LeetCode (focus on easy problems related to Python basics and Pandas). - Identify areas where you need more practice or understanding. - Plan your next steps – perhaps you would like to go more into the field of Machine Learning. - Expected Outcomes: Reinforcement of learned concepts; improved ability to articulate basic data science concepts; a plan for continued learning and interview preparation.