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A lot has happened in the year 2023 and some of you are probably considering transitioning to a career in data science. You may be wondering where to start. What course should I take? Do I need to know something beforehand?
This is where KDnuggets is here to help answer all those questions!
The KDnuggets team has created a path to data science for all our readers to benefit from, regardless of their social status.
Do you want to know more?
Link: Data Science and Python Programming Fundamentals
In the first week, we will learn everything about Python, data manipulation and visualization.
Day 1-3: Python Basics for Aspiring Data Scientists
- An introduction to Python's role in data science.
- A beginner's guide to Python syntax, data types, and control structures.
- Interactive coding exercises to solidify your understanding.
Day 4: Python Data Structures Demystified
- Learn the core data structures of Python with our step-by-step guide. You will learn about lists, tuples, dictionaries, and sets, each with practical examples and their importance in data processing.
Day 5-6: Practical Numerical Computing with NumPy and Pandas
- Discover the power of NumPy and Pandas for numerical analysis and data manipulation, including real-world applications and practical exercises.
Day 7: Data Cleaning Techniques with Pandas
- Equip yourself with essential data cleansing skills using Pandas.
Link: Database, SQL, Data Management and Statistical Concepts
Moving on to the second week, we will learn about databases, SQL, data management and statistical concepts.
- Day 1: Introduction to databases in data science
- Day 2: Introduction to SQL in 5 steps
- Day 3: Data Management Principles for Data Science
- Day 4: Working with Big Data: tools and techniques
- Day 5: Statistics in Data Science: Theory and Overview
- Day 6: Application of descriptive and inferential statistics in Python
- Day 7: Hypothesis testing and A/B testing
Link: Introduction to machine learning
Moving into the third week, we will dive into machine learning.
- Day 1: Demystifying machine learning
- Day 2: Getting started with Scikit-learn in 5 steps
- Day 3: Understanding Supervised Learning: Theory and Overview
- Day 4: Supervised Learning Practice: Linear Regression
- Day 5: Unveiling unsupervised learning
- Day 6: Practice with Unsupervised Learning: K-Means Clustering
- Day 7: Machine Learning Evaluation Metrics: Theory and Overview
Link: Advanced topics and implementation
Moving into the third week, we will delve into advanced topics and implementation.
- Day 1: Exploring neural networks
- Day 2: Introduction to Deep Learning Libraries: PyTorch and Lightening ai
- Day 3: Introduction to PyTorch in 5 steps
- Day 4: Building a convolutional neural network with PyTorch
- Day 5: Introduction to natural language processing
- Day 6: Deploy your first machine learning model
- Day 7: Introduction to Cloud Computing for Data Science
Link: Cloud Deployment
Moving on to the bonus week:
- Bonus 1: Introduction to Google Platform in five steps
- Bonus 2: Deploying your machine learning model to production in the AWS cloud
And just like that, you've been on a 5-week journey to boost your career in data science! The KDnuggets team hopes to have equipped you with the knowledge and tools you need to advance your career in data science!
Tell us what you liked in the comments!
nisha arya is a data scientist and freelance technical writer. She is particularly interested in providing professional data science advice or tutorials and theory-based insights into data science. She also wants to explore the different ways in which artificial intelligence can benefit the longevity of human life. A great student looking to expand her technological knowledge and writing skills, while she helps guide others.
