Certified Data Science Professional

Overview of Data Science & Industry Applications

Basics of Python programming for data science

Python data structures: lists, tuples, sets, dicts

Control structures: loops, conditionals, functions

Intro to NumPy, Pandas, and Matplotlib libraries

Data manipulation with Pandas: import & clean

Types of data: structured, unstructured, semi-structured

Data collection: web scraping, APIs, repositories

Handling missing data: imputation techniques

Data transformation: normalization, scaling

Outlier detection: identifying and handling

Feature engineering: creation and selection

Descriptive statistics: mean, variance, standard deviation

Data distribution: understanding & visualizing

Correlation & covariance between variables

Visualizing data: bar plots, histograms, box plots

Best practices for exploratory data analysis

Storytelling with data insights & visualizations

Supervised vs. unsupervised learning methods

Regression: linear & multiple regression models

Classification: logistic, decision trees, KNN

Model evaluation: cross-validation, confusion matrix

Handling overfitting & underfitting in models

Hyperparameter tuning for model optimization

Clustering techniques: k-means, hierarchical clustering

Dimensionality reduction: PCA & t-SNE

Association rule learning & Apriori algorithm

Ensemble methods: random forests, boosting

Model interpretation: SHAP, LIME for insights

Recommender systems: collaborative filtering

Planning a real-world data science project

Defining a problem and formulating hypotheses

Data acquisition: collection & cleaning

Model selection, training & evaluation

Presenting project findings & key takeaways

Industry applications: finance, healthcare, retail