With data science revolutionizing industries across the globe, it is one of the most
sought-after jobs today. But how can you cut through the sea of skills and tools and
knowledge you need to succeed in this arena? In this roadmap, I take you through steps
you can take to tackle data science, not just manage, but master it.
Data Science is built on programming. It gives you the power to write, test and deploy
data solutions. Learning programming languages aren’t just valuable for manipulating
data, they also give you the tools to implement powerful machine learning models.
Python: Because it is easy and very well supported libraries (NumPy, pandas,
matplotlib).
R: Rich visualization capabilities, and ideal for statistical computing and graphics.
SQL: Used crucially for executing queries of structured data in the relational database.
On that, I simply begin with basic syntax and work my way up to libraries that will be of
use with data manipulation and analysis.
Solve real world problems like cleaning datasets or building simple analytics
dashboards and practice.
improve your coding by using platforms such as Codecademy, LeetCode, and Kaggle.
The data models are something that you will need to understand and having a strong
foundation in mathematics is a requirement for that. Building algorithms, optimizing
models and deriving meaningful insights is what it does.
Statistics: You will learn about distributions, hypothesis testing and Bayesian inference.
Algebra: Learn about matrices, vectors and their operations. Included are eigenvalues
and eigenvectors.
Differential Calculus: So, focus on gradients and how to optimize using gradient
descent.
Discrete Math: Of course, we need to understand logical reasoning and algorithm
design.
Basic to advanced concepts from Khan Academy.
YouTube channel 3Blue1Brown for visually intuitive math explanations.
Every data science project lies at the heart of data analysis. It means that you
understand the datasets, clean them up, and discover patterns.
Exploratory Data Analysis (EDA): Techniques to visualize data and use statistical
measures to summarize the main characteristics of data.
Feature Engineering: Extracting meaningful features from raw data which are
understandable to machine learning models.
Data Wrangling: Skills to work with missing values, then take care of duplicates and
any inconsistent data entries.
These are libraries in Python like pandas and NumPy.
For interactive data exploration — Jupyter Notebooks.
Tips on using Tableau for creating dashboards and visual summaries.
Systems that learn from data and make predictions/classifications from that learnt data
is what machine learning (ML) is all about. Data scientists must become indispensable.
Classification and Regression: Techniques of predictive modeling of structured data.
Reinforcement Learning: Creating methodologies that learn via rewards and penalties.
Clustering and Dimensionality Reduce: Unsupervised learning techniques.
Deep Learning: ML subset of neural networks used for image recognition and natural
language processing.
Start with a few short projects that are like predicting house prices, customer
segmentation or building chatbots and experiment with them.
Leverage platforms that have real world datasets and tutorials on, say Kaggle and
Google’s TensorFlow playground
The art of gathering data from websites is called web scraping. One of the things I’ve
found useful with it is that it’s particularly useful when you have unstructured /
unstructured data sources.
Beautiful Soup: For parsing documents in HTML and XML.
Scrapy: A framework for web scraping projects that goes well beyond web scraping.
Urllib: A basic URL operations package written in Python.
Getting product data for price comparison.
Sentiment analysis – gathering news articles.
Collecting job listings to get a snapshot of the job market.
Raw data is translated into visualizations so that it is easily understandable and
impactful, robust for decision making and storytelling purposes.
Tableau: Build interactive dashboards for Business Intelligence.
D3.js: Fully custom data visualizations made with a JavaScript library.
ggplot2: A flexible R package for making complex visualizations.
We design charts and graphs that communicate insights in a meaningful way.
Use scatter plots, heatmaps and bar charts properly.
Try to be clear, simple and engage the reader.
Big data tools are necessary to store, process and analyze large data volumes as the
data volumes grow.
Hadoop: A large dataset management tool.
Spark: Fast computing framework for big data analytics.
MongoDB: A database to store unstructured data.
Learn how to set up a Hadoop cluster or how to work with distributed file systems.
Use Spark for handling streaming or batch processing data.
Business intelligence (BI) is all about transforming your raw data into actionable insights
to drive decisions based on data.
Tableau and Power BI: To make visual dashboards and reports.
QlikView: A powerful exploratory data analytics tool.
A sales performance dashboard should be built.
Make a KPI tracker that can be used by executives.
In other words, to get a role as a data scientist, you need to be able to bridge the gap
between technical, practical and communication.
Portfolio: Including, but not limited to projects showcasing predictive models,
recommendation systems or visual dashboards.
Networking: Attend meetups, conferences or webinars to meet the industry
professionals.
Certifications: Take the necessary courses and certification to have something as
evidence of your skills, e.g. Google Data Analytics, AWS Certified Data Analytics.
Internships: Internships and freelance projects show you firsthand the real world
experience.
It’s a tough journey to becoming a data scientist, but one that gives back exponentially.
In an attempt to clear up the confusion, I developed this roadmap outlining the core
competencies and skills one has to learn to master the field. This roadmap can be used
by beginners, mid-level individuals looking to hone their craft, or by seasoned veterans
who are trying to get introduced to new topics in the field. By following these steps,
you’ll be ready to apply what you have learned in real world data challenges and make
an impact in the field.
Always staying consistent, practicing regularly and never stop learning, the future of
Data science is yours to define.