From Bugs to Big Data: Making the Leap from Testing to Data Science!

1. roadmap For switch from a Testing Domain to data science & ai

2. Topicstobe covered: Beginner Level: 1) Introduction to Python/R for Data Analysis 2) Basics of Statistics and Probability 3) Data Cleaning and Preprocessing Techniques 4) Exploratory Data Analysis (EDA) and Data Visualization 5) Introduction to Machine Learning Algorithms 6) Understanding Regression and Classification Models 7) Basics of SQL for Data Retrieval 8) Introduction to Git and Version Control 9) Fundamentals of Big Data Technologies 10) Introduction to Cloud Computing Services Curated by Curated by

3. Intermediate Level: 1) Advanced Data Manipulation and Feature Engineering 2) Time Series Analysis and Forecasting 3) Dimensionality Reduction Techniques 4) Advanced Machine Learning Algorithms (e.g., Random Forest, SVM, XGBoost) 5) Natural Language Processing (NLP) Fundamentals 6) Deep Learning Concepts and Neural Networks 7) Model Evaluation and Hyperparameter Tuning 8) Data Mining and Clustering Techniques 9) Reinforcement Learning Principles 10) Introduction to Computer Vision Curated by Curated by

4. Advanced Level: 1) Advanced Deep Learning Architectures (e.g., CNN, RNN, GAN) 2) Advanced NLP Techniques (e.g., Word Embeddings, Transformer Models) 3) Unsupervised Learning Algorithms and Techniques 4) Advanced Big Data Technologies (e.g., Hadoop, Spark) 5) Time Series Forecasting with Advanced Models (e.g., ARIMA, LSTM) 6) Bayesian Machine Learning and Probabilistic Graphical Models 7) Advanced Data Engineering and Pipelines 8) Advanced Topics in Reinforcement Learning 9) Advanced Computer Vision Techniques (e.g., Object Detection, Image Segmentation) 10) Ethical Considerations in AI and Data Science Curated by Curated by

5. Month 1 Building Foundation Week 1 Assess and Plan Assess your current skills and knowledge in data science and AI. Identify the specific areas within these fields that interest you the most. Set specific and achievable goals for the next 12 weeks. Curated by Curated by

6. Week 2 Fundamentals of Data Science and AI Familiarise yourself with the basics of data science, including data types, structures, and data manipulation techniques. Understand the fundamentals of machine learning, such as supervised and unsupervised learning, and the types of algorithms commonly used. Week 3 Programming Proficiency Begin learning or strengthening your programming skills, focusing on languages commonly used in data science such as Python and R. Practise implementing basic data manipulation tasks and algorithms. Curated by Curated by

7. Week 4 Data Exploration and Visualization Learn how to explore and visualise data using libraries like Pandas, Matplotlib, and Seaborn. Work on simple projects that involve data analysis and visualisation to solidify your understanding. Courses Offered by Tutort Academy Data Science & Machine Learning Advance AI and
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8. Month 2 Advanced Skills Development Week 5 Intermediate Machine Learning Dive deeper into various machine learning algorithms and their applications. Start implementing simple machine learning models and evaluate their performance. Curated by Curated by

9. Week 6 Understanding Data Science Tools Familiarise yourself with popular data science tools such as Jupyter Notebooks, Anaconda, and GitHub. Learn how to manage projects effectively using these tools. Week 7 Specialisation in AI Choose a specific area of AI that aligns with your interests, such as natural language processing, computer vision, or deep learning. Begin studying the specific tools and techniques relevant to your chosen field. Curated by Curated by

10. Week 8 Projects and Portfolio Building Work on small projects related to data science and AI to build a portfolio. Showcase your projects on platforms like GitHub and create a personal website to highlight your work. Tutort Benefits Special support for foreign students Resume building & Mock Interview Preparations Curated by Curated by

11. Month 3 Practical Applications and Networking Week 9 Real-world Applications Collaborate on real-world data science and AI projects, either through online platforms or local meetups. Apply your skills to solve practical problems and gain hands-on experience. Curated by Curated by

12. Week 10 Online Courses and Certifications Enroll in relevant online courses or certification programs to enhance your knowledge and credentials in data science and AI. Obtain certifications from reputable platforms to demonstrate your proficiency. Week 11 Networking and Community Engagement Attend industry events, webinars, and meetups to connect with professionals in the field. Join online communities and forums related to data science and AI to exchange knowledge and experiences. Curated by Curated by

13. Week 12 Finalising Transition Plan Evaluate your progress and identify any gaps in your knowledge or skills. Refine your resume and cover letter to highlight your newly acquired skills in data science and AI. Start applying for entry-level positions or internships in data science or AI to kickstart your new career. Why Tutort Academy? 100% 350+ 2.1CR Guaranteed Job Referrals Hiring Partners Highest CTC Curated by Curated by

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