Data science involves extracting insights and knowledge from data through various techniques, including statistics, machine learning, and data analysis. It encompasses cleaning and processing data, exploring patterns, and building models to make predictions or uncover hidden patterns. Data scientists use programming languages like Python or R and tools like TensorFlow or scikit-learn to analyze and interpret complex datasets, helping businesses and organizations make informed decisions.
This repository contains all the reading resources provided and I am adding all my projects created during this course.
Warning
I will not share the Quiz answers or test. I am sharing my assignments and projects made during this course in this repository and I am creating this repository for revision.
Note
I'm working on it for final touchup, stay tuned
Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience.
Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
Skills: Databases, Data visualization, Statistical analysis, Predictive modeling, Machine learning algorithms, and Data mining.
Latest languages, Tools and libraries: Python, SQL, Jupyter notebooks, Github, Rstudio, Pandas, Numpy, ScikitLearn, Matplotlib, and more.
- 01 What is Data Science?
- 02 Tools for Data Science
- 03 Data Science Methodology
- 04 Python for Data Science, AI & Development
- 05 Python Project for Data Science
- 10 Applied Data Science Capstone
