Welcome, aspiring data scientists! Whether you’re pursuing a degree in Computer Science or Software Engineering, you’ve taken your first step into the exciting world of Data Science, AI, ML & Deep Learning. In this guide, we’ll comprehensively introduce you to the field of web development, its scope, industry value, and its future prospects.
What is Data Science?
Data Science is a field in Computer Science which deals with large amounts of data and how to process it to get valuable information and how to take actions upon that information. Data Science is a very wide field. This field includes its sub-fields like Artificial Intelligence, Natural Language Processing, Computer Vision, etc. Artificial Intelligence itself is a broad field which includes the most hyped fields of our time Machine Learning and Deep Learning. All in All, Data Science is a broader field which refers to all the cool fields you have heard of. This field includes hell lot of Maths and Algorithms.
Why is Data Science Important?
Everyone knows the importance of Data Science. It gives us the following benefits:
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Data Insights: In today’s digital age, we have tonnes of data coming from billions of users world-wide every second. All that data is precious for marketers and businesses but it can’t be processed by humans. Hence, they need sophisticated algorithms to work on them and get the results. That is the main market share of Data Science field.
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Human-Like Decision making: Algorithms in data science help us create the machines which can process and prioritize data input almost same as humans do. That is the best labor work you can get out of transistors.
Scope & Industry Value
The industry value of data science is rising exponentially and it seems to not be stopping anytime soon. Following fields are the main market shares for Data Science
- Machine Learning: Machine learning is a field of artificial intelligence which mimics the human brain structure inside computer chips. And feed them data like humans. It is being used in hell lot of fields like Online Trading, Automated Weapons, Simulations, Robots, etc.
- Natural Language Processing: The hype of ChatGPT, Bard, and other language models seems really unrealistic. But it is the reality. Natural Language Processing has emerged as one of the main fields in Data Science and it has applications in fields beyond our imagination.
- Deep Learning: Deep Learning is sub-field of Machine Learning which uses Deep CNNS, transformers, etc., to understand the complex trends in data which cannot be understood by normal humans. This field is much in demand in the industry. The developers bascially don’t have a clue what is going on inside the model. They generate the model with multiple layer neural networks and train them on data and check if they give the right predictions and results. If yes, the model is good to go.
Getting Started with Data Science
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- Data Camp Subscription
Resource: DataCamp
- What You’ll Learn: DataCamp offers a wide range of courses covering data science topics. It provides hands-on experience in data analysis, machine learning, and more. It’s an excellent platform to build a strong foundation in data science.
- Why It’s Necessary: DataCamp provides interactive courses and projects that teach practical skills required for real-world data science work. It’s a valuable resource for beginners and experienced data scientists.
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- Python Basics
- What You’ll Learn: This comprehensive tutorial covers Python basics, from variables and data types to control structures and functions.
- Why It’s Necessary: Python is the primary programming language for data science, and this course is crucial for building a strong coding foundation.
[Udemy] One Week Python [Colt Steele]
Don't waste your time with 60+ hour behemoth courses that you never finish! Instead, try this quick and effective pathway to Python that was designed with your sanity in mind. This course is the p1erfect first step into the world of data science, web dev, machine learning, or any other discipline that requires Python knowledge.
- Python Libraries Basics
- What You’ll Learn: Get familiar with essential Python libraries like numpy, pandas, and matplotlib, which are crucial for data manipulation and visualization.
- Why It’s Necessary: These libraries are the backbone of data analysis and visualization in Python.
- Probability & Statistics
- What You’ll Learn: Dive into probability and statistics, covering topics like probability distributions, hypothesis testing, and regression.
- Why It’s Necessary: Statistics is fundamental to understanding and analyzing data effectively in data science.
[Udemy] Math for Data Science Masterclass [Jose Portilla]
Working together for the first time ever, Krista King and Jose Portilla have combined forces to deliver you a best in class course experience in how to use mathematics to solve real world data science problems. This course has been specifically designed to help you understand the mathematical concepts behind the field of data science, so you can have a first principles level understanding of how to use data effectively in an organization.
- Calculus
- What You’ll Learn: Gain a strong understanding of calculus, which is often used in machine learning algorithms and data science research.
- Why It’s Necessary: Calculus is essential for advanced data science and machine learning applications.
[Udemy] Become a Calculus 1 Master [Krista King]
This 292-lesson course includes video and text explanations of everything from Calculus 1, and it includes 76 quizzes (with solutions!) and an additional 19 workbooks with extra practice problems, to help you test your understanding along the way. Become a Calculus 1 Master is organized into the following sections: Limits & Continuity Derivatives Applications of Derivatives
- Linear Algebra
- What You’ll Learn: Master linear algebra concepts, which are vital for understanding and implementing machine learning algorithms.
- Why It’s Necessary: Linear algebra is the foundation for machine learning models and data manipulation.
[Udemy] Become a Linear Algebra Master [Krista King]
HOW BECOME A LINEAR ALGEBRA MASTER IS SET UP TO MAKE COMPLICATED MATH EASY: This 247-lesson course includes video and text explanations of everything from Linear Algebra, and it includes 69 quizzes (with solutions!) and an additional 12 workbooks with extra practice problems, to help you test your understanding along the way. Become a Linear Algebra Master is organized into the following sections:
- Data Handling with Pandas
- What You’ll Learn: Explore data handling using the pandas library, including data cleaning, manipulation, and exploration.
- Why It’s Necessary: Efficient data handling is a core skill in data science to prepare data for analysis and modeling.
- Data Visualization
- What You’ll Learn: Master data visualization with seaborn and matplotlib, enabling you to create compelling visualizations to convey insights.
- Why It’s Necessary: Data visualization is essential for communicating findings and making data-driven decisions.
- Machine Learning Basics
- What You’ll Learn: Explore the fundamentals of machine learning, including supervised and unsupervised learning, model evaluation, and hyperparameter tuning.
- Why It’s Necessary: Machine learning is at the heart of data science, and this course equips you with the necessary skills to build and evaluate models.
[Udemy] Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023] [Kirill Eremenko]
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. Over 900,000 students world-wide trust this course. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
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- Machine Learning Intermediate
- What You’ll Learn: After grasping the basics, delve into machine learning projects to gain practical experience and deepen your understanding.
- Why It’s Necessary: Real-world projects help you apply your knowledge and gain hands-on experience.
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- Neural Networks
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Resource: TensorFlow 2.0 Complete Course – Python Neural Networks for Beginners Tutorial
- What You’ll Learn: Explore neural networks and deep learning using TensorFlow 2.0. You’ll understand the basics of artificial neural networks.
- Why It’s Necessary: Deep learning is essential for advanced data science, enabling you to work with complex data and models.
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- Deep Learning Basics
- Deep Learning Basics
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- What You’ll Learn: This course covers the fundamentals of deep learning, neural networks, and various deep learning applications.
- Why It’s Necessary: Deep learning is a critical aspect of data science, especially for complex pattern recognition and prediction tasks.
[Udemy] Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus [Kirill Eremenko]
Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.
