Guide To Becoming a Data Scientist 2023 /2024

A data scientist provides the tools and techniques to extract meaningful insights from this data, enabling informed decision-making. They work closely with other professionals such as business leaders, IT professionals and other domain experts. Data science professionals have become increasingly in demand due to the vast amounts of data being generated daily. Businesses who want to gain a competitive edge over their competitor and improve their operations, need to have qualified data scientists.

Step by Step Guide

1. Programming Languages

The main programming languages a beginner should learn include: Python, R, and SQL. You need a solid foundation in programming, so start learning at least one of the languages above. Under programming, you will learn about data structures (e.g., dictionaries, data types, lists, sets, tuples), searching and sorting algorithms, logic, control flow, writing functions, object-oriented programming, and how to work with external libraries.

2. Problem Solving and Project Building

When you get familiarity on the above programming languages, you need to apply the knowledge through problem solving through project building and tackling data challenges.
You will need to gain experience in data collection from various sources such as APIs, databases, publicly available data repositories, and even web scraping from permitted sites. You will use various libraries for data cleaning and manipulation such as Pandas and NumPy, to help turn the information from raw, unformatted data to ready-to-analyze data.

3. Story telling Using Data

Once the data is ready for analysis, you need proficiency in data visualization tools and libraries such as Tableau, Matplotlib, Seaborn among others. You will uncover data insights using these tools and need to know how to communicate these insights effectively to other non-technical stakeholders. Strong communication skills is a huge aspect of how you rely information gained from the data, therefore you need: business acumen by practicing to writing concise and clear reports, business-related blogs, and presentations, dashboard development skills to construct dashboards that summarize or aggregate data to help management make informed actionable decisions and exploratory data analysis knowledge to handle missing values, outliers, and univariate and multi-variate analysis.

4. Statistical Knowledge

Statistical methods are an integral part of data science, since most data science interviews focus on inferential and descriptive statistics. Mathematics and statistics smooth the road to a better understanding of how algorithms work.
You should focus on mastering the following:

a). Descriptive Statistics: Learn about location estimates (mean, median, mode, trimmed statistics, and weighted statistics), and variability used to describe data.

b). Inferential statistics: This form of statistics involves defining business metrics, A/B tests, designing hypothesis tests, and analyzing collected data and experiment results using confidence intervals, p-value, and alpha values.

c). Linear Algebra and Single and Multi-Variate Calculus: These subjects help you better understand gradient, loss functions, and optimizers used in machine learning.

5. Machine learning

As you develop your skills, you can now advance to learn about Artificial intelligence and Machine Learning. These topics fall mainly into three categories:

a). Reinforcement Learning: This discipline helps you build self-rewarding systems. If you want to understand reinforcement learning, learn how to optimize rewards, create Deep Q-networks, and use the TF-Agents library, to name a few.

b). Supervised Learning: This discipline covers regression and classification problems. It would help if you studied simple linear regression, logistic regression, multiple regression, KNNs, polynomial regression, naive Bayes, tree models, and ensemble models. Round out your studies by learning about evaluation metrics.

c). Unsupervised Learning: Unsupervised learning features applications such as clustering and dimensionality reduction. Take deep dives into hierarchical clustering, K-means clustering, PCA, and gaussian mixtures.

Explore various resources about Machine learning such as books like Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

6. Version Control Tools

Learn about Git and GitHub if you need to collaborate on data analysis or code development. Git is a version control system that lets you manage and keep track of your source code history. GitHub is a cloud-based hosting service that lets you manage Git repositories. GitHub hosts your source code projects in a variety of different programming languages and keeps track of the various changes made to every iteration. You can showcase your projects, document and integrate with other cloud platforms using these version control tools

7. Bonus

You have now gained domain knowledge that will help you manage various data science tasks and even possibly qualify for a data science entry level job. To practice what you've learned, work on projects, participate in hackathons, apply for internships and contribute to other open source projects that interest you. You need to also track your progress, this way, you know what you've already covered and you can better visualize what you need to do next. As you advance, you can also start learning about data storage and management systems such as MySQL, MongoDB, and PostgreSQL and cloud computing platforms such as AWS, Azure, and Google Cloud Platform.