1 / 11

Transition From Mechanical Engineering to Data Science | Tutort Academy

At first look, data science and mechanical engineering appear to be separate professions, although they are interconnected in various ways. In the age of digital transformation, incorporating data science concepts and approaches into mechanical engineering has become increasingly important.

Raushan12
Download Presentation

Transition From Mechanical Engineering to Data Science | Tutort Academy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Step-by-Step guide to transition from Mechanical Engineering To Data Science Curated by

  2. Topics to be covered: BeginnerLevelTopics: 1) Fundamentals of Data Science and AI 2) Introduction to Python and its Libraries (NumPy, Pandas) 3) Basics of Statistics and Probability 4) Understanding Linear Algebra for Data Analysis 5) Exploratory Data Analysis (EDA) Techniques 6) Introduction to Machine Learning Concepts IntermediateLevelTopics: 1) Data Visualization using Matplotlib and Seaborn 2) Advanced Data Manipulation in Python 3) Supervised and Unsupervised Learning Algorithms 4) Model Evaluation and Selection Techniques 5) Feature Engineering and Data Preprocessing Methods 6) Implementing Regression and Classification Models Curated by

  3. Advanced Level Topics: 1) Deep Learning and Neural Networks 2) Natural Language Processing (NLP) Techniques 3) Computer Vision and Image Processing 4) Time Series Analysis and Forecasting 5) Reinforcement Learning and its Applications 6) Advanced Model Optimization and Hyperparameter Tuning 7) Big Data Analytics and Distributed Computing Courses Offered by Tutort Academy Data Analytics and
 Business Analytics Program Data Science and
 Artificial Intelligence Program Learn more Learn more Curated by

  4. Month 1: Building Foundation Week 1: Understanding Fundamentals Research and understand the basic concepts of data science and AI. Familiarise yourself with programming languages such as Python, R, and relevant libraries (NumPy, Pandas, etc.). Start learning about statistics, probability, and linear algebra. Curated by

  5. Week 2: Online Courses and Tutorials Enrol in online courses such as Coursera's "Machine Learning" by Andrew Ng or "Python for Data Science" by University of Michigan. Begin working on introductory projects using datasets available online. Week 3: Advanced Courses and Tutorials Delve deeper into the courses and tutorials, focusing on practical applications and exercises. Explore additional learning resources such as Kaggle competitions or data science blogs. Week 4: Consolidating Knowledge Review the concepts covered in the previous weeks and ensure a solid understanding of the fundamentals. Attempt small coding exercises and quizzes to reinforce learning. Curated by

  6. Month 2: Skill Development Week 5: Advanced Programming and Tools Dive deeper into Python for data manipulation, visualisation, and analysis. Learn about data visualisation tools such as Matplotlib and Seaborn. Week 6: Intermediate Python Projects Work on intermediate-level projects to apply the Python skills acquired. Experiment with different datasets and explore data manipulation techniques. Curated by tutort academy Curated by

  7. Week 7: Introduction to Machine Learning Begin learning about various machine learning algorithms, including regression, classification, and clustering. Understand the basic concepts of model evaluation and selection. Week 8: Implementing Machine Learning Algorithms Implement simple ML models and algorithms on sample datasets. Explore feature engineering and data preprocessing techniques. Tutort Benefits 1:1 Mentorship from Industry experts 24x7 Live 1:1 Video based doubt support Curated by

  8. Month 3: Practical Application Week 9: Small-scale Data Science Projects Start working on small-scale data science projects related to mechanical engineering problems. Analyse real-world datasets and derive insights using the skills acquired. Week 10: Community Engagement and Feedback Join online communities and forums to discuss your projects and gain feedback. Participate in discussions and contribute to the community with your insights. Curated by

  9. Week 11: Specialisation and Advanced Learning Choose a specific area of AI or data science, such as natural language processing or computer vision. Delve deeper into the concepts and explore advanced learning materials. Week 12: Certification and Portfolio Building Pursue relevant certifications or online specialisation courses from reputable platforms. Develop a strong portfolio showcasing your projects and skills. Network with professionals in the data science and AI field and seek mentorship opportunities. Why Tutort Academy? 1250+ 350+ 2.1CR Career Transitions Hiring Partners Highest CTC Curated by

  10. Ensure that you have a strong grasp of the beginner level topics before progressing to the intermediate and advanced levels. Continuous practice, hands-on projects, and engagement with the data science community will help solidify your understanding and application of these concepts. All the best Curated by

  11. Start Your Upskilling with us Explore More Explore our courses Data Science and
 Artificial Intelligence Program Data Analytics and
 Business Analytics Program www.tutort.net Watch us on Youtube Read more on Quora Follow us on

More Related