Download to read offline
More Related Content
Similar to Data Science curriculum
Data Science curriculum
- 1. Python for DataScience Statistics For Data Science Data Analysis with Python Data Visualization with python Machine Learning Information Technology Solutions STATISTICS FOR DATA SCIENCE Module 1 –Analytics Problem Solving Introduction Business Problem – Definition & Understanding Preparing Data for Analysis Model Evaluation and deployment Module 2 – Inferential Statistics Basics of Probability Discrete Probability Distribution Continuous Probability Distribution Central Limit Theorem Module 3 – Hypotheis Testing Concepts of Hypothesis Testing I Concepts of Hypothesis Testing II Industry Demonstration of Hypothesis Testing Hypothesis Testing Additional Resources Object Automation Software Solutions Pvt. Ltd. Data Science Foundations ABOUT THE COURSE The course covers the concepts and tools you need throughout the entire data science pipeline, In the final project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will receive IBM Badge with the completion certificate. PERFORMANCE EVALUATION Every specialization includes a hands-on performance evaluation. You’ll need to successfully clear the evaluation process to complete the specialization. PYTHON FOR DATA SCIENCE Module 1 - Python Basics Your first program Types Expressions and Variables String Operations Module 2 - Python Data Structures Lists and Tuples Sets Dictionaries Module 3 - Python Programming Fundamentals Conditions and Branching Loops Functions Objects and Classes Module 4 - Working with Files in Python Reading files Writing files Working with and Saving data
- 2. Object Automation SoftwareSolutions Pvt. Ltd. technology consulting RECOMMENDED SKILLS None TIME TO COMPLETE 30 HOURS SERVICE AVAILABLE Technical Support Installation and Setup Maintenance Application Support Hardware Support Guaranteed Warranty For more information or services please visit us on the Web at: www.object-a DATA VISUALIZATION WITH PYTHON Module 1 - Introduction to Visualization Tools Introduction to Data Visualization Introduction to Matplotlib Basic Plotting with Matplotlib Dataset on Immigration to Canada Line Plots Module 2 - Basic Visualization Tools Area Plots Histograms Bar Charts Module 3 - Specialized Visualization Tools Pie Charts Box Plots Scatter Plots Bubble Plots Module 4 - Advanced Visualization Tools Waffle Charts Word Clouds Seaborn and Regression Plots RECOMMENDED SKILLS None TIME TO COMPLETE 40 HOURS EARN A CERTIFICATE When you finish every modules and complete the performance evaluation, you’ll earn a certificate. DATA ANALYSIS WITH PYTHON Module 1 - Importing Datasets Learning Objectives Understanding the Domain Understanding the Dataset Python package for data science Importing and Exporting Data in Python Basic Insights from Datasets Module 2 - Cleaning and Preparing the Data Identify and Handle Missing Values Data Formatting Data Normalization Sets Binning Indicator variables Module 3 - Summarizing the Data Frame Descriptive Statistics Basic of Grouping ANOVA Correlation More on Correlation Module 4 - Model Development Simple and Multiple Linear Regression Model Evaluation Using Visualization Polynomial Regression and Pipelines R-squared and MSE for In-Sample Evaluation Prediction and Decision Making
- 3. Object Automation SoftwareSolutions Pvt. Ltd. SERVICE AVAILABLE Technical Support Installation and Setup Maintenance Application Support Hardware Support Guaranteed Warranty For more information or services please visit us on the Web at: www.object-automation.com technology consulting MACHINE LEARNING TECHNIQUES Module 1 - Introduction Machine Learning Modelling Flow How to treat Data in ML Parametric & Non-parametric ML Algorithm Types of Machine Learning Scikit-Learn Library Module 2 – Linear Regression Introduction to Linear Regression Linear Regression using Gradient Descent Linear Regression using OLS Linear Regression using Stochastic Gradient Descent Module 3 –Logistic Regression Introduction to Logistic Regression K Nearest Neighbor Understanding KNN Choosing K Distance Metrics - Euclidean, Manhattan, Chebyshev Module 4 –Decision Tree Decision Tree Fundamental concepts of Ensemble Hyper-Parameters Bagging - Extra Trees, Random Forest Boosting - AdaBoost, Gradient Boosting Module 5 –SVM Vector Support Vector Machines (SVM) Understanding Hyperplane Perceptron Algorithm SVM Kernels SVM Optimization Applications of SVM Module 5 –Clustering What is Clustering? K-means Algorithm Types of Clustering Evaluating K-means Clusters Principal Component Analysis (PCA)