Data Analytics
Enroll for this Data Analytics course that equips you with the knowledge and skills needed to utilize and optimize data for making informed decisions. This Data Analytics online course is designed for beginners as well as professionals wanting to learn more about the use of data in the business landscape. You’ll be exploring a wide range of techniques, from descriptive statistics to advanced predictive modeling.
Other than understanding the fundamentals of data, you’ll also learn data preprocessing and description & summarizing techniques. Discover techniques for Text, Web, and Social Media Analysis. By the end of the training course, you’ll be fluent in statistical methods, machine learning algorithms, and data mining techniques.
What You`ll Learn on This Course
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- Understanding data types, it’s application & importance
- Describe statistics including univariate, bivariate, and multivariate analysis
- Expertise in data cleaning and preparation techniques
- Clustering algorithms to group similar data
- Identify frequent data mining patterns
- Regression analysis to build predictive/forecast models
- Data categorization into different classes
- Learn ensemble learning, algorithm bias, and non-binary classification tasks
- Analyzing text, web, and social media data
This course comes with:
Curriculum
- 2 Sections
- 0 Lessons
- 26 Weeks
Expand all sectionsCollapse all sections
- Lesson Plan
Introduction
- How this Course is Organized
- Who Should Read this Course
2 What Can We Do With Data?
- Big Data and Data Science
- Big Data Architectures
- Small Data
- What is Data?
- A Short Taxonomy of Data Analytics
- Examples of Data Use
- A Project on Data Analytics
3 Descriptive Statistics
- Scale Types
- Descriptive Univariate Analysis
- Descriptive Bivariate Analysis
- Final Remarks
4 Descriptive Multivariate Analysis
- Multivariate Frequencies
- Multivariate Data Visualization
- Multivariate Statistics
- Infographics and Word Clouds
- Final Remarks
5 Data Quality and Preprocessing
- Data Quality
- Converting to a Different Scale Type
- Converting to a Different Scale
- Data Transformation
- Dimensionality Reduction
- Final Remarks
6 Clustering
- Distance Measures
- Clustering Validation
- Clustering Techniques
- Final Remarks
7 Frequent Pattern Mining
- Frequent Itemsets
- Association Rules
- Behind Support and Confidence
- Other Types of Pattern
- Final Remarks
8 Cheat Sheet and Project on Descriptive Analytics
- Cheat Sheet of Descriptive Analytics
- Project on Descriptive Analytics
9 Regression
- Predictive Performance Estimation
- Finding the Parameters of the Model
- Technique and Model Selection
- Final Remarks
10 Classification
- Binary Classification
- Predictive Performance Measures for Classification
- Distance‐based Learning Algorithms
- Probabilistic Classification Algorithms
- Final Remarks
11 Additional Predictive Methods
- Search‐based Algorithms
- Optimization‐based Algorithms
- Final Remarks
12 Advanced Predictive Topics
- Ensemble Learning
- Algorithm Bias
- Non‐binary Classification Tasks
- Advanced Data Preparation Techniques for Prediction
- Description and Prediction with Supervised Interpretable Techniques
13 Cheat Sheet and Project on Predictive Analytics
- Cheat Sheet on Predictive Analytics
- Project on Predictive Analytics
14 Applications for Text, Web and Social Media
- Working with Texts
- Recommender Systems
- Social Network Analysis
Appendix A
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
0 - Hands-on LAB Activities
What Can We Do With Data?
- Extracting Knowledge from Data Using the KDD Process
2 Descriptive Statistics
- Creating a Scatter Plot
- Creating an Area Chart
- Creating a Line Chart
- Creating a Bar Chart
- Creating a Pie Chart
- Creating a Basic Histogram
3 Descriptive Multivariate Analysis
- Creating a Box Plot
- Creating a Heatmap
4 Data Quality and Preprocessing
- Detecting Outliers
- Normalizing Data
- Transforming Data for Improved Data Summarization
5 Clustering
- Performing K-Means Clustering
6 Frequent Pattern Mining
- Implementing Frequent Itemset Mining Using R
- Implementing the Apriori Algorithm
- Implementing the Eclat Algorithm
7 Regression
- Performing Simple Linear Regression
8 Classification
- Implementing the Naive Bayes Algorithm
- Performing Logistic Regression
9 Additional Predictive Methods
- Performing Multivariate Adaptive Regression Splines
- Implementing SVM for Regression
10 Advanced Predictive Topics
- Implementing the AdaBoost Method
- Performing One-Class Classification
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