Hey, you! You’re really motivated and passionate about Learning and that’s your superpower. As sir Nelson Mandela once Said, “Education is the most powerful Weapon which you can use to change the world.” You’re here because you want to become a Data Scientist, and this 30 days roadmap is your launchpad.
What is a Data Scientist? A Data scientist is professional always look for better understanding of Data Extracting Insights and Knowledge from structured and unstructured data using Scientific Methods, Algorithms, and Systems. The Statistics are the key aspects of Analyzing Vast Amount of Data with Domain Expertise to Interpret and Analyze Complex data problems. Key responsibilities of Data Scientist is to Gain Insights, Make Data-Driven Decisions, Optimize operations, and predict future outcomes.
guys, In simpler terms a Data Scientist is like a detective who uses data instead of clues to solve problems, tell stories, and forecast what might happen next ,few of the physical tasks include:
- Cleaning the Data And Preparing the Data
2. Analyzing Trends and Patterns
3. Building predictive models using machine Learning
4. Reviewing The results through visualizations and Reports
What Sectors Mostly Require Data Scientists?
across the world and Industries are Finance, Healthcare, Marketing, Technology & IT Tech companies ,Retail and Ecommerce, Manufacturing &Supply Chain ,Educational Institutions, government &public sector.
- Technology & IT Tech MNCs like Google, Amazon, and Facebook etc., Enhence User experiences, and improve algorithms.
- Manufacturing & Supply Chain Manufacturers produce vas amount of Data so, use models for predict maintenance, quality control, optimization of Data.
- Healthcare Data Scientists in healthcare analyzes patient records, predict disease, supports by giving personalized medicine through data driven insights.
- Educational institutes uses to Analyze students performance, enhance learning results and activities.
Let’s dive into our 30-Day Plan!
Week 1: Fundamentals & Tools Setup in PC
- search deeply what is data science and why it matters now a days.
- learning python basics like variables, loops, functions, conditional statements.
- Installation of Essential tools like: Anaconda, Jupyter Notebook, vs code
- create account in git hub
- check google colab and explore some kaggle Datasets
Week 2: Data Handling & Analysis
- Learn python libraries like pandas for data manipulation for 2 days
- Understanding data types, indexing and slicing for 1 day
- perform data cleaning like Handling Missing values, duplicates for 1 day
- Learn data visualization with matplotlib for 2 day
- Mini-Project: Clean any real time data from kaggle
Week 3:Machine Learning Basics
- know about supervised VS Unsupervised learning for 1 day
- learn Scikit-learn : To Train and Test Models for 1 day
- build ML model like linear Regression, classification 2 days
- understanding of ML models for evaluation of metrics 1 day
- practice the Data sets with Iris, Housing dataset for 2 days
Week 4:Projects & portfolio Building
- simple project: Build an ML project
- Document your project and submit in Git hub
- explore data sets in kaggle competitions