The Certified Data Science Professional course by SkillFloor is designed to equip learners with essential data science skills, from understanding core statistical concepts to applying machine learning algorithms and data visualization techniques. Through this course, students will master tools like Python, R, and Tableau, and gain hands-on experience working with real-world data sets. Whether you're new to data science or looking to advance your skills, this course offers a comprehensive learning path to become proficient in data manipulation, predictive modeling, and business analytics.
What sets this course apart is its practical, project-based approach and the expert guidance provided by seasoned professionals in the field. Learners will not only gain in-depth theoretical knowledge but also build a portfolio through real-world projects and case studies, making them job-ready upon completion. With a recognized certification and guaranteed internship placement, graduates of this course will be well-prepared to pursue careers as data scientists, analysts, or business intelligence professionals in diverse industries.
₹60,000
The Certified Data Science Professional course culminates in a recognized certification that validates your expertise and skills in data science. This certification demonstrates your ability to analyze complex data, apply machine learning techniques, and derive meaningful insights from data sets. It enhances your resume and signals to employers that you have completed a comprehensive training program focused on practical applications and industry standards. Earning this certification can significantly boost your career prospects, opening doors to various opportunities in the rapidly growing field of data science.
The Data Science Internship by SkillFloor, in collaboration with Rubixe – a company working in artificial intelligence and digital solutions – is designed to help students and beginners gain real-world experience in data science. The internship focuses on practical learning, where participants work on real datasets and apply tools like Python, Pandas, and Scikit-learn. Interns learn how to collect, clean, and analyze data, as well as how to build basic machine learning models. This hands-on approach helps learners understand how data science is used to solve problems in businesses and technology.
By working on projects inspired by Rubixe’s work in AI, interns get to see how data-driven decisions are made in companies. They also learn how to present their findings in a clear and useful way, which is a key part of any data science job. The internship not only builds technical skills but also improves problem-solving, communication, and confidence. It's a strong starting point for anyone looking to begin a career in data science or artificial intelligence.
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