Data science is a multidisciplinary field that combines mathematics, statistics, programming and domain knowledge to extract valuable insights from data. If you are a beginner looking to embark on a journey in this field, this roadmap will help you navigate through the key concepts, tools and skills necessary to become a proficient data scientist.
Understanding the basics
Mathematics : linear algebra, calculus and probability theory
Statistics : concepts like probability distributions,
hypothesis testing and regression analysis
Master programming skills
programming languages like python and R are commonly used in
Python programming language in particular offers a variety of libraries for data science, which include; pandas for data analysis, numpy for numerical calculations and scientific computing and matplotlib for visualizations.
Data Acquisition And Cleaning
Learn Structured Querry Language (SQL) to retrieve and manipulate
data from databases.
You need to understand data preprocessing techniques, dealing
with missing values and handling outliers.
Exploratory Data Analysis EDA
While performing EDA , you use libraries like pandas, seaborn and
plotly to explore and visualize data.
Machine learning Fundamentals
Machine learning is key as models are trained using machine learning algorithms. These algorithms are either supervised or unsupervised. Supervised learning involves use of labelled data while in unsupervised learning the data is unlabelled.
Scikit learn is a popular library for implementing machine learning models.
Gain expertise in the industry you want to apply data science to. It is useful for meaningful analysis.
Learn Git for version control to manage your projects effectively and collaborate with others.
Master data visualization tools such as Tableau and Power Bi to communicate your findings effectively.