Data Science for Beginners: 2023–2024 Complete Roadmap.

Data Science for Beginners: 2023–2024 Complete Roadmap.

What is Data Science?

In very simple terms, Data Science is the study of data with the intention of extracting meaningful insights from the data and then using those insights to make data-informed decisions, mostly for businesses and organizations.

A more technical definition, I especially like how IBM gives the definition of Data Science:

“Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.”

Why learn Data Science?

With the definition out of the way, why then do you need to learn Data Science?

• Data is the new oil. You might have heard this somewhere and it may sound like an overstretch but truer words have never been spoken. In the 21st Century, Data is the new driving force behind industries and organizations that have tapped into drawing insights from their customer data, consensually of course, are far ahead of competition.

• Pool of opportunities. It goes without saying that data is at the center of any industry you may think of. Some of the industries that have really embraced data science include Healthcare, Fintech and E-commerce.

• **Lucrative career. **This shouldn’t be the main reason driving you to get into Data Science but the fact is there’s pretty fair compensation in the Data Science industry. According to Glassdoor, the average salary for a Data Scientist is $117,345/yr.

• **Use Data to do good. **Adversities can be detected and avoided by the insights gotten from building predictive models. For example in healthcare, there are a number of models built for detection of some serious complications like heart failure which can predict the chance of a person’s heart developing complications based on some inputs. As a result, a person can learn from the insights and change their lifestyle, consequently avoiding getting the disease in the long run.

The Roadmap.

In order to break into the field of Data Science, there are some basic Foundations that are a must have. These include:

Mathematics

1. Linear Algebra

2. Probability and Statistics

3. Calculus

Programming

1. Python —(“Introduction to Python for Data Science”)

• Programming Syntax in Python

• Functions

• Data Structures (lists, tuples, dictionaries)

• Object Oriented Programming

1. R

Data Manipulation

1. Numpy

2. Pandas

3. Dplyr( R Programming)

Data Visualization

1. Matplotlib

2. Seaborn

3. Ggplot2 (R)

Data Preprocessing and Exploration.

1. Exploratory Data Analysis

2. Feature Engineering

3. Data Cleaning

4. Handling Missing Data

5. Data Normalization

Git and GitHub.

As a data scientist, your work often involves collaborating with fellow data scientists on various projects. During these collaborations, you need to make updates to specific sections of the code. This is where Git and GitHub play a pivotal role in enhancing workflow efficiency.

I have a detailed article on Git and GitHub for Data Scientists, “Comprehensive Guide to GitHub for Data Scientists.”

SQL.

SQL is one of the most important tools that a data scientist should be well versed with. It gives the Data Scientist the ability to retrieve and filter data, manipulate data, aggregate and summarize data, join data.

I have a detailed article on SQL, “Essential SQL commands that are a must know for a data scientist.”

Machine Learning.

1. Supervised Learning

• Regression
  _Linear Regression
  _Polynomial Regression

• Classification
  _Logistic Regression
  _Support Vector Machines
  _Decision Trees
  _K-Nearest Neighbors
  _Random Forest

1. Unsupervised Learning

• Clustering
  _K-Means Clustering
  _Hierarchical Clustering
  _DBSCAN

• Dimensionality Reduction
  _Principal Component Analysis (PCA)
  _T- Distributed Stochastic Neighbor Embedding (t-SNE)
  _Linear Discriminant Analysis (LDA)

1. Reinforcement Learning

2. Model Evaluation and Validation

• Cross- Validation

• Hyperparameter Tuning

• Model Selection

1. Python Libraries

• Scikit-learn

• Tensorflow

• Pytorch

• Keras

Deep Learning.

• Neural Networks
  _Perceptron
  _Multi-Layer Perceptron

• Convolutional Neural Networks (CNNs)
  _Image Classification
  _Object Detection
  _Image Segmentation

• Recurrent Neural Networks (RNNs)
  _Sequence-to-Sequence Models
  _Text Classification
  _Sentiment Analysis

• Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
  _Time Series Forecasting
  _Language Modeling

• Generative Adversarial Networks (GANs)
  _Image Synthesis
  _Style Transfer
  _Data Augmentation

Data Visualization and Reporting.

• Dashboarding Tools
  _Tableau
  _Power BI
  _Dash (Python)
  _Shiny (R)

• Storytelling with Data

Must have Soft Skills.

• Be a Problem- Solver

• Effective Communication Skills

• Time Management

• Teamwork

Keep Learning.

As a Data Scientist, similar to all other fields in tech, you will be a forever- learner. There will always be emerging trends, frameworks and languages and you have to stay up-to-date to be an effective Data Scientist.

Some ways to keep you up to date and in a loop of continuous learning include:

1. Doing online courses.

2. Work on projects. You can get datasets which are readily available on platforms like Kaggle.

3. Solving online challenges like leetcode and Hackerrank.

4. Reading Data Science Books and Research papers.

5. Reading Informative Articles and Blogs.

6. Networking through Meetups both online and Physical.