From Development to Data Science: A Complete Roadmap to Transition Seamlessly

1. Roadmap to switch from development To Data Science

2. Topics to be covered: Month 1: Laying the Foundation 1) Introduction to Data Science and AI 2) Fundamentals of Statistics and Probability 3) Python for Data Science 4) R for Data Science Month 2: Building Specialized Skills 1) Data Cleaning and Preprocessing 2) Data Analysis and Visualization 3) Supervised Learning Algorithms 4) Unsupervised Learning and Clustering Month 3: Application and Specialization 1) Advanced Machine Learning Techniques 2) Deep Learning Fundamentals 3) Advanced Deep Learning and Neural Networks 4) Real-world Projects and Networking Curatedby

3. Month 1: Laying the Foundation Week 1: Introduction to Data Science and AI Definition and significance of data science and AI Understanding the role of data scientists and AI professionals in various industries Exploring different types of data and their relevance in the field tutort academy Curated by Curated by

4. Week 2: Fundamentals of Statistics and Probability Basic concepts of statistics, including mean, median, mode, variance, and standard deviation Understanding probability distributions and their applications in data analysis Exploring hypothesis testing and its importance in drawing conclusions from data Week 3: Python for Data Science Introduction to Python and its applications in data science Learning basic data structures and functions in Python Exploring libraries such as NumPy and pandas for data manipulation and analysis tutort academy Curated by Curated by

5. Week 4: R for Data Science Introduction to the R programming language and its applications in statistical computing and graphics Learning data manipulation and visualisation using the dplyr and ggplot2 packages Understanding the integration of R with other data science tools and platforms Why Tutort Academy? 100% 350+ 2.1CR Guaranteed Job Referrals Hiring Partners Highest CTC tutort academy Curated by Curated by

6. Month 2: Building Specialized Skills Week 1: Data Cleaning and Preprocessing Understanding the importance of data cleaning and preprocessing in data analysis Exploring techniques for handling missing data, outliers, and data normalisation Implementing data cleaning workflows using Python and R Week 2: Data Analysis and Visualization Exploring exploratory data analysis (EDA) techniques to understand data patterns and relationships Creating meaningful visualisations using libraries such as Matplotlib, Seaborn, and ggplot2 Communicating insights effectively through data visualisation best practices tutort academy Curated by Curated by

7. Week 3: Supervised Learning Algorithms Understanding the concepts of supervised learning and its various algorithms such as linear regression, logistic regression, and decision trees Implementing supervised learning algorithms using scikit-learn and caret in Python and R, respectively Evaluating model performance and tuning hyperparameters for better predictions Week 4: Unsupervised Learning and Clustering Exploring unsupervised learning techniques such as clustering and dimensionality reduction Implementing clustering algorithms like K-means, hierarchical clustering, and DBSCAN Understanding the importance of feature scaling and dimensionality reduction techniques in unsupervised learning tutort academy Curated by Curated by

8. Month 3: Application and Specialization Week 1: Advanced Machine Learning Techniques Exploring advanced machine learning algorithms such as support vector machines (SVM), random forests, and gradient boosting Understanding ensemble methods and their applications in improving model performance Implementing advanced ML techniques using Python's scikit-learn and R's caret packages Week 2: Deep Learning Fundamentals Understanding the basics of neural networks and their applications in deep learning Exploring deep learning frameworks such as TensorFlow and PyTorch Implementing simple neural network architectures for tasks such as image classification and natural language processing tutort academy Curated by Curated by

9. Week 3: Advanced Deep Learning and Neural Networks Exploring advanced neural network architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) Understanding the concepts of transfer learning and fine-tuning pre-trained models Implementing complex deep learning projects for various applications Week 4: Real-world Projects and Networking Engaging in real-world data science and AI projects to gain hands-on experience and build a strong portfolio Collaborating with industry professionals on open- source projects or internships to gain practical insights and experience Understanding the importance of feature scaling and dimensionality reduction techniques in unsupervised learning tutort academy Curated by Curated by

10. By following this comprehensive and in-depth 3-month roadmap, you will be well-equipped with the necessary skills and knowledge to successfully transition from development to the dynamic and rapidly growing field of Data Science and AI. Continuously building upon this foundation and staying curious about emerging technologies and best practices will further drive your success in this exciting domain. All the best tutort academy Curated by Curated by

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