Data Science.

Supervised vs. Unsupervised Learning: Distinguish between different machine learning approaches.

Regression and Classification: Build models using algorithms like linear regression and logistic regression.

Clustering and PCA: Apply clustering techniques and principal component analysis for data reduction.

Decision Trees and Ensembles: Understand and implement decision trees, random forests, and gradient boosting.

Support Vector Machines: Utilize SVMs for complex classification tasks.

Neural Networks: Get introduced to neural networks using Keras for deep learning applications.

Natural Language Processing (NLP): Process and analyze textual data for insights.

Time Series Analysis: Work with time-dependent data and forecasting models.

Ethical Data Use: Understand the importance of ethics in data science and responsible data handling.

Data Storytelling: Craft narratives that make your data insights accessible to stakeholders.

Presentation Skills: Learn to present complex data in a clear and compelling manner.

Tailored Communication: Adapt your message for technical and non-technical audiences.

Role Clarity: Learn what data scientists do and the impact they have across industries.

Essential Skills: Identify and develop the key skills required for a successful data science career.

Data Importation: Import data from various sources using Pandas.

Data Structures: Understand and manipulate different data types in Python.

Data Cleaning: Handle missing values, anomalies, and prepare data for analysis.

Statistical Summaries: Compute and interpret basic summary statistics.

Data Subsetting: Select and manipulate subsets of data for targeted analysis.

Pattern Recognition: Identify trends, outliers, and insights within datasets.

Visualization Principles: Learn what makes an effective data visualization.

Plot Selection: Choose the right plot types for your data.

Visualization Tools: Create compelling visuals using Seaborn and Plotly in Python.