Day 3: Tasks for Aspiring Data Scientist, Data Engineer, and Cloud Engineer

Ekemini ThompsonEkemini Thompson
5 min read

Table of contents

Day 3 for Aspiring Data Scientist: Data Cleaning and Preprocessing with Pandas

Objective: Master the art of data cleaning and preprocessing using Pandas. Today’s focus will be on handling missing data, outliers, and applying transformations that prepare data for analysis and modeling.

Task Overview: For Day 3, write an article titled "Data Cleaning and Preprocessing with Pandas: A Practical Guide". The article should explain the common challenges of messy data and demonstrate how to clean and preprocess data efficiently using Pandas.

Task Steps:

  1. Research:

    • Explore common data cleaning techniques, including handling missing values, removing duplicates, and managing outliers.

    • Learn how to apply transformations like scaling, encoding categorical variables, and normalizing data using Pandas.

  2. Write the Article:

    • Title: Use the title "Data Cleaning and Preprocessing with Pandas: A Practical Guide".

    • Introduction: Briefly explain why data cleaning is crucial for effective data analysis and modeling.

    • Main Content:

      1. Handling Missing Data: Explain methods like dropna(), fillna(), and how to impute missing values.

      2. Removing Duplicates: Show how to identify and remove duplicate rows using drop_duplicates().

      3. Managing Outliers: Discuss how to detect and handle outliers with techniques like removing them or using transformation methods.

      4. Scaling and Normalization: Demonstrate how to scale or normalize data using methods like MinMaxScaler or StandardScaler in Pandas.

      5. Encoding Categorical Data: Provide examples of converting categorical data into numerical format using pd.get_dummies() or label encoding.

    • Conclusion: Emphasize the importance of clean, well-structured data for accurate data analysis.

    • Links: Include links to tutorials or resources on data preprocessing in Pandas.

  3. Hands-On Practice:

    • Use a public dataset to apply these preprocessing techniques in real-life scenarios. Choose a messy dataset from Kaggle or UCI to practice data cleaning.

    • Share code snippets and outputs in the article to guide readers.

  4. Publish:

    • Post the article on Medium or Dev.to and share a summary on LinkedIn and Twitter. Upload a PDF version on Academia.edu.

  5. Reflection:

    • Write a brief reflection (200-300 words) on what you learned about data cleaning and how it fits into the data science process.

Day 3 for Aspiring Data Engineer: Introduction to Data Warehousing

Objective: Learn the basics of data warehousing, an essential concept for managing large-scale datasets. Today’s focus will be on understanding how data warehouses differ from databases and their role in the ETL pipeline.

Task Overview: For Day 3, write an article titled "Introduction to Data Warehousing: Key Concepts and Architecture". The goal is to explain the role of data warehouses in data engineering and highlight how they support large-scale data analysis.

Task Steps:

  1. Research:

    • Study what data warehouses are and how they differ from traditional databases.

    • Learn about common data warehouse architectures, such as Star Schema and Snowflake Schema, and tools like Amazon Redshift, Google BigQuery, and Snowflake.

  2. Write the Article:

    • Title: Use the title "Introduction to Data Warehousing: Key Concepts and Architecture".

    • Introduction: Introduce the concept of data warehousing and its role in supporting business intelligence and large-scale data analytics.

    • Main Content:

      1. What is a Data Warehouse?: Define a data warehouse and its main purpose.

      2. Data Warehouse vs. Database: Explain the differences between data warehouses and traditional databases.

      3. Data Warehouse Architectures: Introduce the Star Schema and Snowflake Schema and explain their structures.

      4. Popular Data Warehouse Tools: Discuss tools like Amazon Redshift, Google BigQuery, and Snowflake, highlighting their use cases and features.

      5. ETL and Data Warehousing: Explain how data warehouses fit into the ETL process.

    • Conclusion: Emphasize the significance of data warehousing for data engineers.

    • Links: Include links to data warehousing tutorials or documentation.

  3. Hands-On Practice:

    • Sign up for a free-tier service like Amazon Redshift or Google BigQuery and explore how to set up a basic data warehouse.

    • Document the process of creating a data warehouse and loading sample data for querying.

  4. Publish:

    • Post the article on Medium or Dev.to and share a summary on LinkedIn and Twitter. Upload a PDF version on Academia.edu.

  5. Reflection:

    • Write a brief reflection (200-300 words) on what you learned about data warehousing and its relevance to large-scale data projects.

Day 3 for Aspiring Cloud Engineer: Understanding Cloud Networking Basics

Objective: Grasp the fundamentals of cloud networking. Today’s focus will be on learning about Virtual Private Cloud (VPC) and how networking works in cloud infrastructure, especially on platforms like AWS.

Task Overview: For Day 3, your task is to write an article titled "Understanding Cloud Networking: An Introduction to AWS VPC". This article should explain the key concepts of cloud networking and provide a basic introduction to AWS VPC.

Task Steps:

  1. Research:

    • Study the basics of cloud networking, focusing on Virtual Private Clouds (VPCs) and how they enable secure, scalable network environments in the cloud.

    • Learn about key components such as subnets, route tables, Internet Gateways, and NAT Gateways.

  2. Write the Article:

    • Title: Use the title "Understanding Cloud Networking: An Introduction to AWS VPC".

    • Introduction: Explain why networking is a crucial part of cloud infrastructure and introduce AWS VPC as a service for setting up isolated cloud networks.

    • Main Content:

      1. What is Cloud Networking?: Define cloud networking and its role in enabling secure, scalable cloud infrastructure.

      2. Introduction to AWS VPC: Explain the concept of Virtual Private Cloud (VPC) and its purpose.

      3. VPC Components: Discuss key components of VPCs, such as subnets, route tables, Internet Gateways, and NAT Gateways.

      4. Creating a VPC: Provide a step-by-step guide to setting up a basic VPC on AWS, configuring subnets, and managing routing.

    • Conclusion: Emphasize the importance of understanding cloud networking for cloud engineers.

    • Links: Include links to official AWS VPC documentation and tutorials.

  3. Hands-On Practice:

    • Create a free-tier AWS VPC. Configure basic networking settings and test connectivity with a simple EC2 instance.

    • Document the steps involved in setting up your VPC and share screenshots to visualize the process.

  4. Publish:

    • Post the article on Medium or Dev.to and share a summary on LinkedIn and Twitter. Upload a PDF version on Academia.edu.

  5. Reflection:

    • Write a brief reflection (200-300 words) on what you learned about cloud networking and how VPCs work in cloud infrastructure.

These Day 3 tasks will deepen your understanding of essential tools and concepts in data science, data engineering, and cloud computing, helping you develop practical skills and share knowledge through writing and publishing.

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Ekemini Thompson
Ekemini Thompson