Image by Author

Join KDnuggets with our Back to Basics pathway to get you kickstarted with a new career or a brush up on your data science skills. The Back to Basics pathway is split up into 4 weeks with a bonus week. We hope you can use these blogs as a course guide. 

If you haven’t already, have a look at Week 1: Back to Basics Week 1: Python Programming & Data Science Foundations

Moving onto the second week, we will learn about Database, SQL, Data Management and Statistical Concepts.

• Day 1: Introduction to Databases in Data Science
• Day 2: Getting Started with SQL in 5 Steps
• Day 3: Data Management Principles for Data Science
• Day 4: Working with Big Data: Tools and Techniques
• Day 5: Statistics in Data Science: Theory and Overview
• Day 6: Applying Descriptive and Inferential Statistics in Python
• Day 7: Hypothesis Testing and A/B Testing

Introduction to Databases in Data Science

Week 2 - Part 1: Introduction to Databases in Data Science

Understand the relevance of databases in data science. Also learn the fundamentals of relational databases, NoSQL database categories, and more.

Data science involves extracting value and insights from large volumes of data to drive business decisions. It also involves building predictive models using historical data. Databases facilitate effective storage, management, retrieval, and analysis of such large volumes of data.

So, as a data scientist, you should understand the fundamentals of databases. Because they enable the storage and management of large and complex datasets, allowing for efficient data exploration, modelling, and deriving insights.

Getting Started with SQL in 5 Steps

Week 2 - Part 2: Getting Started with SQL in 5 Steps

When it comes to managing and manipulating data in relational databases, Structured Query Language (SQL) is the biggest name in the game. SQL is a major domain-specific language which serves as the cornerstone for database management and provides a standardized way to interact with databases. 

With data being the driving force behind decision-making and innovation, SQL remains an essential technology demanding top-level attention from data analysts, developers, and data scientists.

This comprehensive SQL tutorial covers everything from setting up your SQL environment to mastering advanced concepts like joins, subqueries, and optimising query performance. With step-by-step examples, this guide is perfect for beginners looking to enhance their data management skills.

Data Management Principles for Data Science

Week 2 - Part 3: Data Management Principles for Data Science

Understanding key data management principles that data scientists should know.

Through your journey as a data scientist, you will come across hiccups, and overcome them. You will learn how one process is better than another, and how to use different processes depending on your task at hand. 

These processes will work hand-in-hand, to ensure that your data science project goes as effectively as possible and plays a key component in your decision-making process. 

Working with Big Data: Tools and Techniques

Week 2 - Part 4: Working with Big Data: Tools and Techniques

Where do you start in a field as vast as big data? Which tools and techniques to use? We explore this and talk about the most common tools in big data.

Long gone are times in business when all the data you needed was in your ‘little black book’. In this era of the digital revolution, not even the classical databases are enough.

Handling big data became a critical skill for businesses and, with them, data scientists. Big data is characterized by its volume, velocity, and variety, offering unprecedented insights into patterns and trends.

To handle such data effectively, it requires the usage of specialized tools and techniques.

Statistics in Data Science: Theory and Overview

Week 2 - Part 5: Statistics in Data Science: Theory and Overview

High-level exploration of the role of statistics in data science.

Are you interested in mastering statistics to stand out in a data science interview? If it’s yes, you shouldn’t do it only for the interview. Understanding Statistics can help you in getting deeper and more fine-grained insights from your data.

In this article, I am going to show the most crucial statistics concepts that need to be known for getting better at solving data science problems.

Applying Descriptive and Inferential Statistics in Python

Week 2 - Part 6: Applying Descriptive and Inferential Statistics in Python

As you progress in your data science journey, here are the elementary statistics you should know.

Statistics is a field encompassing activities from collecting data and data analysis to data interpretation. It’s a study field to help the concerned party decide when facing uncertainty.

Two major branches in the statistics field are descriptive and Inferential. Descriptive statistics is a branch related to data summarization using various manners, such as summary statistics, visualization, and tables. While inferential statistics are more about population generalization based on the data sample.

Hypothesis Testing and A/B Testing

Week 2 - Part 7: Hypothesis Testing and A/B Testing

The pillars of data-driven decisions.

In an era where data reigns supreme, businesses and organizations are constantly on the lookout for ways to harness its power.

From the products you’re recommended on Amazon to the content you see on social media, there’s a meticulous method behind the madness.

At the heart of these decisions? A/B testing and hypothesis testing.

But what are they, and why are they so pivotal in our data-centric world? Let’s discover it all together!

Wrapping it Up

Congratulations on completing week 2!!

The team at KDnuggets hope that the Back to Basics pathway has provided readers with a comprehensive and structured approach to mastering the fundamentals of data science. 

Week 3 will be posted next week on Monday - stay tuned!
 
 

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.