Data Science Course
Best Data Science Course from GeekBase, Exclusively designed for Working Professionals. with our expert-guided training and 100% Placement Assistance.
Course overview
Unlocking Data Science: From Python Foundations to Cutting-Edge Machine Learning Techniques
Course Description:
Embark on a comprehensive Data Science journey with GeekBase Technology. This course covers everything from Python basics to advanced machine learning, including data analysis, visualization, and predictive modeling. Gain hands-on experience with tools like Pandas, build models using regression and classification, and apply your skills through real-world projects. Join GeekBase Technology’s Data Science course and start your path to a successful career.
Key Highlights:
Target Audience:
Ambitious data enthusiasts eager to build a strong foundation in Data Science.
Students aiming to excel in data analysis, machine learning, or AI-driven careers.
Professionals looking to elevate their expertise with advanced Data Science tools and methodologies.
Mentor Support:
Learners will have access to an experienced instructor who will provide support through one on one meeting, live Q&A sessions, and email to answer questions and provide guidance throughout the course.
Curriculum
13 modulesModule 1: Python Programming Foundations
- Introduction to Python: history, variables, data types.
- Control flow: loops and conditionals.
- Data structures: lists, tuples, dictionaries.
- Functions, error handling, and memory management.
- File handling: reading and writing files.
Module 2: Numerical Computing with NumPy
- Introduction to NumPy: arrays, creation, and operations.
- Array indexing, slicing, and reshaping.
- Mathematical operations and broadcasting.
- Aggregation, statistical functions, and advanced array operations.
Module 3: Data Handling with Pandas
- Introduction to Pandas: DataFrames and Series.
- DataFrame operations: indexing, selection, filtering.
- Data manipulation: sorting, grouping, and aggregating.
- Handling missing data and imbalanced datasets.
- Merging, joining, and concatenating DataFrames.
Module 4: Data Visualization Techniques
- Introduction to Matplotlib: basic plots and customization.
- Advanced plotting with Seaborn: statistical and categorical plots.
- Interactive visualizations with Plotly: 3D plots and time series.
- Data storytelling and effective visualization techniques.
Module 5: Databases and Exploratory Data Analysis
- Introduction to MongoDB: setup, CRUD operations, and querying.
- Introduction to MySQL: setup, SQL queries, and advanced techniques.
- Exploratory Data Analysis (EDA): objectives, techniques, and descriptive statistics.
Module 6: Probability and Statistical Analysis
- Basics of probability: conditional probability and distributions.
- Bayes theorem and applications.
- Probability distributions: normal, binomial, Poisson.
- Hypothesis testing: p-values, confidence intervals, and significance.
Module 7: Feature Engineering and Data Preparation
- Handling missing data and outliers.
- Feature extraction and creation: transformations and scaling.
- Data encoding: one-hot encoding and label encoding.
- Techniques for handling imbalanced datasets.
- Understanding covariance and correlation.
Module 8: Supervised Learning – Regression
- Introduction to supervised learning: regression and classification.
- Linear regression: concepts, implementation, and regularization.
- Evaluation metrics: MSE, RMSE, R-squared.
- Real-world regression problems and case studies.
Module 9: Supervised Learning – Classification
- Logistic regression: concepts and implementation.
- Evaluation metrics: accuracy, precision, recall, F1-score.
- K-Nearest Neighbors (KNN) and Decision Trees.
- Advanced techniques: random forests, gradient boosting.
- Classification case studies and real-world applications.
Module 10: Unsupervised Learning and Dimensionality Reduction
- Introduction to unsupervised learning: K-means clustering.
- Clustering applications and visualizations.
- Dimensionality reduction with PCA.
- Practical applications and handling high-dimensional data.
Module 11: Image and Text Data Analysis
- Basics of image data: formats, loading, and processing.
- Feature extraction from images.
- Text data analysis: tokenization, stop word removal, lemmatization, and stemming.
- Syntactic and semantic text analysis.
Module 12: Advanced Natural Language Processing (NLP)
- Introduction to NLP: tokenization and syntactic analysis.
- Part-of-Speech (PoS) tagging and Named Entity Recognition (NER).
- Text normalization: lemmatization and stemming.
- Semantic analysis: word embeddings and similarity.
- Sentiment analysis and text classification projects.
Module 13: Data Visualization Tools and Career Development
- Introduction to Power BI: data transformation and visualization.
- Advanced Power BI features: DAX, data modeling, and filtering.
- Introduction to Tableau: visualization techniques and interactivity.
- Exercise using Power BI or Tableau
Certification
Course Certification:
Upon successful completion of the course, there will be cumulative test conducted and students who scored above 60% marks will receive a certificate of completion from GeekBase Technology, which can be used to showcase their newly acquired Data Science skills.
Note: Test will be a MCQ pattern and maximum two attempts allowed.
Why certified Data Scientist ?
