Congratulations on taking your first step toward the Data Science field. Data science helps us discover actionable insights and build predictive models that support decision-making. In this article, I am going to take you through the basics of data science 2023-2024 and get comfortable with the journey.

Step1: Setting up your environment

• Install the latest Python environment that will help you run Python code.

• Install SQL Database and SQL server. There are different types of SQL databases but they perform the same function. Choose one that best suits you.

• On notebooks, there are various types of notebooks available for data scientists and machine learning practitioners. The main ones used are Jupiter notebooks, Google Colab and Kaggle notebooks. The choice of notebooks depends on programming language preference, collaboration requirements and the nature of your tasks.

  Step2: Understand Key areas

Make sure you learn this key area of data science to have a strong foundation.

• Learn Mathematics and statistics

Have a strong understanding of linear algebra, calculus, probability and statistics.

• Learn programming

Learn the basics of Python and R programming languages which are commonly used in data science. Mostly, python is preferred for its efficiency and effectiveness.

By starting with Python, start by learning Python syntax such as indentation, variables and data types. Also learn Python libraries such as matplotlib, seaborn and Tableau.

• Data handling

Learn how data collected is handled before being manipulated. As a data scientist, you will be required to preprocess data before splitting it for training and evaluation.

Step3: Data visualization

Familiarize yourself with different data visualization tools such as Matplot, seaborn and Tableau. Learn how to create effective visualizations to carry your insights.

Step4: Learn Machine Learning basics

Machine Learning forms part of data science. Learn the different types of algorithms of machine learning such as supervised learning, unsupervised learning and reinforcement learning.

Familiarize yourself with classification and regression which are categorized under supervised learning. Also, learn clustering which is under unsupervised learning.

Step5: Data Science Tools

Particular tools will form an important part when manipulating models. Learn frameworks such as TensorFlow and PyTorch for deep learning.

Explore sci-kit-learn for machine learning tasks.

Step6: Learn Data Analysis Techniques

In this step, learn exploratory data analysis techniques to discover patterns and insights in data.

Practice hypothesis testing and A/B testing for statistical analysis.

Step7: Get Familiar with Advanced topics

After getting comfortable with fundamentals, learn advanced machine learning workloads such as neural networks, NLP and computer vision.

Step8: Build Real-world Projects

Having gained enough knowledge, get your hands on building projects using real datasets. This will help you build a strong portfolio that will stand out to recruiters.

Consider joining the Kaggle community and participating in competitions to gain a strong portfolio.

Step9: Career Preparation

Craft a resume and LinkedIn profile showcasing your projects and skills. Start applying for internships or entry-level data-related jobs.

This roadmap serves as a starting point for a data science career. Consider joining online communities such as Github, Stack Overflow and LinkedIn groups to stay up-to-date with the latest trends. Your dedication and consistency in learning will pave the way for a successful journey into data science in 2023-2024, All the best!!!