Roadmap to become Data scientist .

-

Here's a more detailed roadmap for becoming a data scientist, including specific chapters/topics to cover and recommended platforms for practical use:


Year 1:

1. Month 1-2: Understand the Role and Mathematics

   - Introduction to Data Science

   - Basic Mathematics for Data Science


Platforms: Coursera, edX, Khan Academy


2. Month 3-5: Programming and Data Manipulation

   - Introduction to Python

   - Python for Data Analysis

   - Exploratory Data Analysis


Platforms: Codecademy, DataCamp, Kaggle


3. Month 6-8: Machine Learning Fundamentals

   - Supervised Learning:

     - Linear Regression

     - Logistic Regression

     - Decision Trees and Random Forests


   - Evaluation Metrics and Cross-Validation

   - Regularization Techniques


Platforms: Coursera (Andrew Ng's Machine Learning course), Kaggle


4. Month 9-10: Data Visualization and Communication

   - Data Visualization Principles

   - Exploratory Data Visualization with Matplotlib and Seaborn

   - Communicating Insights from Data


Platforms: Udemy, Tableau Public, Plotly


5. Month 11-12: Hands-on Projects and Specialization

   - Real-world Projects or Kaggle Competitions

   - Choose a Subfield of Data Science for Specialization:

     - Natural Language Processing (NLP)

     - Computer Vision

     - Time Series Analysis


Platforms: Kaggle, GitHub, Towards Data Science blog


Year 2:

1. Month 1-3: Advanced Machine Learning

   - Unsupervised Learning:

     - Clustering Algorithms

     - Dimensionality Reduction Techniques

   - Advanced Supervised Learning Techniques:

     - Support Vector Machines (SVM)

     - Ensemble Methods


Platforms: Coursera (Andrew Ng's Machine Learning course), Kaggle


2. Month 4-6: Big Data and Distributed Computing

   - Introduction to Big Data Technologies:

     - Apache Hadoop

     - Apache Spark

   - Working with Large Datasets

   - Distributed Computing with Spark


Platforms: Cloudera, Hortonworks, Databricks


3. Month 7-9: Model Deployment and Productionization

   - Model Deployment in Production

   - Containerization with Docker

   - Cloud Platforms for Deployment:

     - AWS (Amazon Web Services)

     - Azure (Microsoft Azure)

     - GCP (Google Cloud Platform)


Platforms: Docker, AWS, Azure


4. Month 10-12: Continual Learning and Specialization Refinement

   - Stay updated with the Latest Advancements in Data Science:

     - Research Papers

     - Industry Blogs

     - Online Communities

   - Advanced Topics within Your Chosen Subfield


Platforms: Medium, ArXiv, Kaggle, Towards Data Science blog



Comments

Post a Comment

Popular posts from this blog

Mediokart: Revolutionizing Access to Quality Healthcare

-
Image
In today’s technology-driven world, healthcare continues to face significant challenges despite advancements. Access to affordable, high-quality healthcare remains a struggle for many, especially in developing regions. Mediokart, an innovative healthcare startup, is addressing this issue by leveraging cutting-edge technology, user-centered design, and a deep understanding of consumer needs. With a clear mission to enhance healthcare accessibility, Mediokart is redefining how people interact with medical solutions. Target Audience Analysis Mediokart’s success is rooted in its understanding of its audience. By segmenting its market, the startup ensures its services cater to the diverse needs of different socio-economic groups: Elite Consumers : Prioritize premium healthcare services with personalized care, seamless experiences, and cutting-edge technology. Upper Middle-Class : Seek high-quality healthcare solutions that balance cost with service, offering value for their investment. Midd...
Read more

The Hidden Climate Cost of Our Dairy and Beef Consumption: An Urgent Call for Change

-
Image
Recent devastating events in India, including the 2023 Himachal Pradesh floods and the unprecedented Delhi floods of 2024, serve as stark reminders of our changing climate. While various factors contribute to climate change, the rapid expansion of dairy and beef production stands out as a significant yet often overlooked contributor to these environmental catastrophes. The Scale of Environmental Impact: Global Evidence According to the Food and Agriculture Organization (FAO), India’s position as the world’s largest milk producer, with over 200 million tons annually, comes with a heavy environmental price. The dairy and beef industries contribute significantly to global greenhouse gas emissions: Livestock farming accounts for approximately 14.5% of global greenhouse gas emissions (UN FAO Report) The dairy sector alone produces 4% of all human-caused greenhouse gas emissions (World Wildlife Fund) A single dairy cow can produce up to 100 kg of methane annually (Environmental Protection Ag...
Read more

A Perfect Start to 2025

-
Today marked the beginning of 2025, and it turned out to be an incredible day filled with productivity, enjoyment, and meaningful moments. My day started on a positive note. After taking a refreshing shower in the morning, I spent some time worshiping and meditating, which brought a sense of calm and focus. However, I did miss out on my workout—something I need to work on as I move forward. The highlight of the day was spending quality time with friends and family. We shared chocolates, exchanged laughter, and visited the nearest temple. These moments of connection were truly heartwarming. Additionally, I made a small effort to give back by donating biscuits to those in need. These acts of kindness added meaning to my day and left me feeling content. On the productive side, I managed to complete several important tasks. I read the first chapter of Atomic Habits , which offered some powerful lessons: Small habits, when repeated consistently, compound into life-changing results. Focus on...
Read more