Data Science Roadmap:

𝗣𝘆𝘁𝗵𝗼𝗻
👉🏼 Master the basics: syntax, loops, functions, and data structures (lists, dictionaries, sets, tuples)
👉🏼 Learn Pandas & NumPy for data manipulation
👉🏼 Matplotlib & Seaborn for data visualization

𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
👉🏼 Descriptive statistics: mean, median, mode, standard deviation
👉🏼 Probability theory: distributions, Bayes' theorem, conditional probability
👉🏼 Hypothesis testing & A/B testing

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
👉🏼 Supervised vs. unsupervised learning
👉🏼 Key algorithms: Linear & Logistic Regression, Decision Trees, Random Forest, KNN, SVM
👉🏼 Model evaluation metrics: accuracy, precision, recall, F1 score, ROC-AUC
👉🏼 Cross-validation & hyperparameter tuning

𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
👉🏼 Neural Networks & their architecture
👉🏼 Working with Keras & TensorFlow/PyTorch
👉🏼 CNNs for image data and RNNs for sequence data

𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
👉🏼 Handling missing data, outliers, and data scaling
👉🏼 Feature selection techniques (e.g., correlation, mutual information)

𝗡𝗟𝗣 (𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴)
👉🏼 Tokenization, stemming, lemmatization
👉🏼 Bag-of-Words, TF-IDF
👉🏼 Sentiment analysis & topic modeling

𝗖𝗹𝗼𝘂𝗱 𝗮𝗻𝗱 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮
👉🏼 Understanding cloud services (AWS, GCP, Azure) for data storage & computing
👉🏼 Working with distributed data using Spark
👉🏼 SQL for querying large datasets

Don’t get overwhelmed by the breadth of topics. Start small—master one concept, then move to the next. 📈

You’ve got this! 💪🏼

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

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ENJOY LEARNING 👍👍