Data Science Roadmap 2024

Programming in Data Science

The important programming languages used in Data Science are Python and R. For beginners, it is advisable to choose Python Programming first. This is because python is more widely used and is the most popular programming language in the Data Science world. It is easy to understand and learners without any engineering background can also excel in learning Python programming. Understanding the importance of Python in data science is the first step toward success.

What are the specific programming topics?

• Learning about Data Structures like Data Types, Lists, Tuples, Dictionary, Array, Sets, Matrices, Vectors, etc.
• Understanding, defining, and writing User Defined Functions.
• Understanding different types of Loops and conditional statements like If, else, etc.
• Understanding algorithms like Searching and Sorting.
• Finally Join, Aggregate, and Merge SQL concepts.

Libraries in Python

There are numerous libraries available that can be used in Python.

• NumPy for numerical calculations
• Pandas for data manipulation
• Matplotlob for data visualization
• Seaborn for advanced data visualization
• SciPy for performing statistical analysis
• Scikit-Learn for machine learning, etc.

R Programming Language

It is another important programming language utilized by data scientists. It is an open-source programming language that is used for classification, clustering, statistical testing, and linear and nonlinear modeling. R language also includes several statistical and graphical tools. R libraries are as follows:

• dplyr: It is used for data wrangling and data analysis. Five functions Select, Filter, Arrange, Mutate, and Summarize are the fundamentals of dplyr package.
• Tidyr: It is used for data cleaning or tidying.
• ggplot2: It is a visualization package.

How does one become a Data Scientist?

1) Understanding Fundamentals of Data Science - Understanding the basics is always important to excel at something. Statistical Analysis, Programming, Data Visualization, and machine learning concepts are required to be learned by every aspiring-to-be data scientist.

2) Meeting all the Educational Requirements - The prerequisites for becoming a successful data scientist are having a mathematical background, understanding statistics, and basic computer programs.

3) Developing Essential Skills - Technical skills like programming languages, data mining, data visualization, data analysis, statistics, machine learning, deep learning, natural language processing, algorithms, data engineering, etc are essential for every data scientist to understand their fingertips.

• Programming languages like Python, SQL, and R.
• Data mining is acquiring relevant information from various sources.
• Data visualization tools like Tableau, Microsoft Excel, and Google Charts are to be understood thoroughly by a Data Scientist.
• Data Analysis involves looking for trends and patterns that can be turned into useful information.
• Statistics aids in the decision-making process.
• Deep learning involves the creation of artificial neural networks.
• NLP is a subset of Machine Learning.
• Algorithms are a set of rules and calculations used to solve issues.
• Data gathering, wrangling, cleaning, etc come under data engineering.

Soft skills like having an analytical mind, business intelligence, critical thinking, adaptability, flexibility, problem-solving mindset, communication skills, etc are also necessary.

4) Understanding important data science tools like Apache Spark, Tableau, SAS, MATLAB, R, and BigML.

5) Working on projects can be challenging for beginners but it is a crucial step for every data scientist as it could lead to a successful career in data science even without experience.

6) It is always important to learn through internships and this learning experience is unmatched. Internships could be paid or unpaid but both allow the learners to gain a tremendous amount of knowledge.

What includes Machine Learning and AI?

Learning machine learning algorithms is important for every data scientist. Supervised learning, Unsupervised learning, and Deep learning are some of the important algorithms.

• Supervised learning: This algorithm can be used when the target variable is present. This includes Regression and Classification techniques. Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, KNNs, etc are included in Supervised Learning.
• Unsupervised learning: In this algorithm, no target variable is available. K-Means Clustering, PCA, Association Mining, etc are considered under Unsupervised learning.
• Deep learning is a subset of machine learning that make use of Neural Networks. Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM), Autoencoders, etc are considered here.