Lvl.2 -> Data Scientist

Pre-requisite: Lvl.1 Data Analyst
Approx. Time Required: 3 months

✅ NEW! Join our Mentorship Program for this Career Path here. 👈

Embark on a transformative journey from data enthusiast to a proficient Data Scientist. Develop your analytical skills, learn to craft compelling data stories with Tableau, code with Python, design databases, apply statistical analysis, and dive deep into machine learning with real exercises. This path is your roadmap to mastering the landscape of data science and unlocking a new realm of career opportunities.

This learning path is not just about acquiring technical skills but also about developing a mindset to approach problems analytically, extract meaningful insights from data, and present findings in a way that influences business strategies. Your journey as a Data Scientist begins here.

1. Python A-Z

Master Python, the versatile language at the heart of data analysis, machine learning, and AI. You will discover how to:

Write Python code for data manipulation and prepare datasets for analysis.
Visualize data with libraries like Matplotlib and Seaborn.

This course will give you a robust set of programming skills to analyze data, build models, and automate data processes.

2. Statistics for Business Analytics and Data Science A-Z

Understand the statistics that underpin high-quality data analysis and business decision-making:

Apply statistical concepts like distributions, hypothesis testing, and the central limit theorem.
Use statistical methods to validate data models and findings.

Gain a deep understanding of statistics to inform data science practices and validate analytical models.

3. Machine Learning A-Z

Dive into the core of machine learning with practical exercises in both Python and R.

Implement various machine learning models from simple linear regression to complex neural networks.
Understand the theory and intuition behind algorithms and apply them to real-world scenarios.

You will get a comprehensive skill set to build, evaluate, and optimize machine learning models for a wide array of data science applications.

Plus, check out these Live Lab Recordings:

Live Lab Recording #1: Handling Imbalanced Datasets and Detecting Outliers

Live Lab Recording #5: Time Series Analysis using Amazon Forecast

Live Lab Recording # 16: Navigating Data Science Interviews: Key Algorithm Techniques

Live Lab Recording # 17: AI Fairness – Risks and Mitigation

Live Lab Recording # 18: Unsupervised Learning – Clustering Techniques – Part 1

Live Lab Recording # 20: Unsupervised Learning – Clustering Techniques – Part 2

Live Lab Recording # 21: Building and publishing a custom Dataset to HuggingFace datasets

Live Lab Recording #24 : Gradient descent from scratch

Live Lab Recording #26: Overview of Generative Adversarial Networks (GANs)

Live Lab Recording #27: Creating an Intelligent agent with Q learning