Data science roadmap ||roadmaps 2024 ||Aiml #shorts #programming #datascience#roadmaps #tech#python

A Data Science Roadmap outlines the key skills, tools, and concepts that someone aspiring to become a data scientist should learn. Here's a summarized version of a typical roadmap:

1. Mathematics & Statistics

Linear Algebra: Vectors, matrices, matrix operations

Calculus: Derivatives, integrals, optimization techniques

Probability: Random variables, distributions, Bayes theorem

Statistics: Descriptive stats, inferential stats, hypothesis testing

2. Programming

Languages: Python and/or R

Libraries:

Python: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras

R: ggplot2, dplyr, caret

Version Control: Git, GitHub

3. Data Handling

Data Collection: APIs, web scraping (e.g., BeautifulSoup, Selenium)

Data Cleaning: Handling missing data, outliers, duplicates

Exploratory Data Analysis (EDA): Visualization, correlation analysis

Data Wrangling: Merging, reshaping, filtering data

4. Data Visualization

Python Libraries: Matplotlib, Seaborn, Plotly

R Libraries: ggplot2, Shiny

Dashboarding: Tableau, Power BI

5. Databases

SQL: Queries, joins, subqueries, aggregations

NoSQL: MongoDB basics, JSON handling

6. Machine Learning

Supervised Learning: Regression (linear, logistic), classification (decision trees, SVMs, k-NN)

Unsupervised Learning: Clustering (k-means, hierarchical), dimensionality reduction (PCA)

Model Evaluation: Train-test split, cross-validation, metrics like precision, recall, F1 score

Model Tuning: Hyperparameter optimization, GridSearch, RandomizedSearch

7. Deep Learning

Neural Networks: Perceptron, feed-forward networks

Advanced Networks: CNNs for image processing, RNNs for sequential data

Frameworks: TensorFlow, PyTorch

8. Natural Language Processing (NLP)

Text Processing: Tokenization, stop words, stemming, lemmatization

Techniques: TF-IDF, word embeddings, sentiment analysis

Advanced NLP: Transformers, BERT, GPT

9. Big Data Tools

Hadoop: MapReduce, HDFS

Spark: PySpark for distributed data processing

10. Cloud Computing & Deployment

Platforms: AWS, Google Cloud, Microsoft Azure

Model Deployment: Flask/Django APIs, Docker, Kubernetes

11. Soft Skills & Domain Knowledge

Communication: Presenting insights clearly to stakeholders

Collaboration: Working with cross-functional teams (e.g., engineering, marketing)

Business Acumen: Understanding the problem context and objectives

By following this roadmap, one can acquire the essential skills needed to become a successful data scientist, progressing from foundational knowledge to more advanced topics over time.