Roshan Thapa

Posted on

Data Science Roadmap with Free Study Material

To become proficient in data science, it's important to follow a structured roadmap that covers the key concepts and skills necessary for success in the field. Here's a suggested data science roadmap:

1- Mathematics and Statistics: Develop a strong foundation in mathematics and statistics, including concepts such as linear algebra, calculus, probability theory, and statistical inference. These mathematical principles form the basis for many data science techniques.

ðŸ“ŒMathematics https://lnkd.in/d7DN5NWX

ðŸ“ŒStatistics- https://lnkd.in/deXUUpby

2- Programming Skills: Learn a programming language commonly used in data science, such as Python or R. Gain proficiency in writing code, data manipulation, and basic programming concepts. Familiarize yourself with libraries and packages specific to data science, such as NumPy, Pandas, and scikit-learn in Python, or dplyr and tidyr in R.

ðŸ“Œ Python for DS - https://lnkd.in/deXUUpby

ðŸ“Œ R for DS - https://lnkd.in/duvmVJZr

ðŸ“ŒNumPy - https://lnkd.in/ds8dZwWq

ðŸ“ŒPandas - https://lnkd.in/d-XnVpqj

ðŸ“ŒScikit-learn - https://lnkd.in/d7_dFPwA

ðŸ“ŒSeaborn - https://lnkd.in/dm2hjMff

ðŸ“ŒTensorFlow - https://lnkd.in/d8QkqkaS or

ðŸ“ŒPytorch - https://lnkd.in/dHnSdaEf

ðŸ“ŒScrapy - https://lnkd.in/dNqrCkAT or

ðŸ“ŒBeautifulsoup - https://lnkd.in/d2-AFmCt

ðŸ“ŒSpaCy - https://lnkd.in/dUJVyRgB

ðŸ“ŒOpenCV - https://lnkd.in/dSbRBcZJ

3- Exploratory Data Analysis (EDA): Learn techniques for data cleaning, data preprocessing, and exploratory data analysis. Gain skills in handling missing data, outliers, and data visualization using tools like Matplotlib, Seaborn, or ggplot2.

ðŸ“Œ EDA - https://lnkd.in/dC_G7WH6

4- Machine Learning: Understand the fundamental concepts of machine learning, including supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), and evaluation metrics. Study popular machine learning algorithms like decision trees, random forests, support vector machines (SVM), and neural networks.

ðŸ“ŒEnglish - https://lnkd.in/dHgrquCT

ðŸ“ŒHindi - https://lnkd.in/dyyTNFpP

5- Data Visualization: Learn effective data visualization techniques to communicate insights and patterns. Master visualization libraries like Matplotlib, Seaborn, ggplot2, or Tableau. Understand the principles of visual perception and design to create visually appealing and informative visualizations.

ðŸ“ŒData Visualization: - https://lnkd.in/dgK8QgWB

6- Statistical Modeling: Deepen your knowledge of statistical modeling techniques, such as linear regression, logistic regression, time series analysis, and hypothesis testing. Learn how to interpret model results and evaluate model performance.

ðŸ“ŒStatistical Modelling- https://lnkd.in/dKXfE3km

7- Feature Engineering: Explore feature engineering techniques to transform and extract meaningful features from raw data. Gain knowledge in feature selection, dimensionality reduction, and creating new features to improve model performance.

ðŸ“Œ English- https://lnkd.in/dKHXd8hB

ðŸ“Œ Hindi - https://lnkd.in/dp9e4xpi

8- Big Data and Distributed Computing: Familiarize yourself with handling big data using frameworks like Apache Hadoop and Apache Spark. Learn distributed computing concepts and how to scale data processing and analysis.

ðŸ“ŒBig Data & Hadoop - https://lnkd.in/dNhYaZYh

9- Natural Language Processing (NLP): Understand the basics of NLP and learn techniques for text preprocessing, sentiment analysis, text classification, and language modelling. Explore libraries like NLTK, spaCy, or Transformers.

ðŸ“ŒHindi - https://lnkd.in/d4C5J2Wu

10- Deep Learning: Dive into deep learning techniques, including neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and deep learning frameworks like TensorFlow or PyTorch.

ðŸ“ŒDeep Learning - https://lnkd.in/dzGY97Dd

11- Data Science Lifecycle and Projects: Gain experience working on end-to-end data science projects, from problem formulation to data collection, analysis, modelling, and deployment. Practice building models, interpreting results, and communicating findings effectively.

ðŸ“ŒLifecycle- https://lnkd.in/dwpuyNs6

ðŸ“ŒTop Data Science Projects With Source Code - https://lnkd.in/dK6PX7nG

ðŸ“Œ180 Data Science and Machine Learning Projects - https://lnkd.in/duf34TY2

ðŸ“ŒGithub- https://lnkd.in/dNrQ66Qj

12- Continued Learning and Professional Development: Stay updated with the latest advancements and techniques in data science. Participate in online courses, attend conferences, join data science communities, and read research papers to expand your knowledge and network with peers.

Remember, data science is a vast and evolving field, and the roadmap may vary based on your background, interests, and industry requirements. It's crucial to continuously learn, practice, and stay curious to excel in the field of data science.