Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Applied Data Science for Data Analysts is an online Databricks-offered programme designed for intermediate-level learners. The online certification is the final course in the three-course Data Science with Databricks for Data Analysts Specialization. Thus, to be eligible for taking the online programme, the learners must have completed the first two courses in the specialization. Applied Data Science for Data Analysts Certification Course, provided jointly by Coursera and Databricks, will help the learners to enrich their data science, and applied data science with python and machine learning skills and help the student to become masters of applied data science.
The learners passionate about learning about apple data science can enrol and complete the Applied Data Science for Data Analysts Training within approximately 16 hours. As the Applied Data Science for Data Analysts Certification by Coursera is completely offered in the online mode, the candidates can opt for the programme at their convenience and pace from anywhere.
The Highlights
- Provided by Coursera
- Offered by Databricks
- Self-Paced Learning Option
- Intermediate level course
- 100% Online Course
- Around 16 Hours to Complete
- Flexible Deadlines
- Shareable Certificate
- Financial Aid Available
Programme Offerings
- English videos with multiple subtitles
- Shareable Certificate
- Financial aid available
- Shareable Certificates
- Self-Paced Learning Option
- Course Videos & Readings
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
The Applied Data Science for Data Analysts Certification Fee is determined based on the number of months the learners want to stay on the programme and complete the programme. The month-wise course can be played in the EMI option and the refund period of 14 days is also provided by Coursera.
Applied Data Science for Data Analysts Fee Structure
Duration | Amount in INR |
1 Month | INR 4,091 |
3 Months | INR 8,182 |
6 Months | INR 12,274 |
Eligibility Criteria
Certification Qualifying Details
The sharable certification of the Applied Data Science for Data Analysts online course will be provided for the students at the end of the programme. For that, the learners are required to cover the course proceeding successfully and pay the course fee.
What you will learn
Applied Data Science for Data Analysts Certification Syllabus will help the learners to build the capacity and knowledge to deal with data with the help of unsupervised machine learning, make use of the tree-based models for solving complex supervised learning issues, and master the application process of apply cross-validation strategies and hyperparameter tuning.
Who it is for
Applied Data Science for Data Analysts Classes is highly recommended for learners like Data analysts, Data Engineers, and Data scientists.
Admission Details
Step 1 -Browse the official URL : https://www.coursera.org/learn/applied-data-science-for-data-analysts
Step 2- Kickstart the programme by clicking ‘Enroll Now’.
The Syllabus
Videos
- Course introduction
- Review of Data Science
- Review of Machine Learning
- Data Science Process vs. Machine Learning Workflow
- Introduction to Databricks (Optional)
- Introduction to the Platform (Optional)
- Introduction to Apache Spark (Optional)
- Introduction to Delta Lake (Optional)
Readings
- Before you begin
- Hands-on with Databricks Lab (Optional)
Quiz
- Course Introduction and Prerequisites
Discussion Prompt
- Introduce Yourself!
Videos
- Lesson Introduction
- Exploring Data
- Visualizing Data
- Introduction to K-means Clustering
- Applied K-means Clustering
- Identifying the Number of Clusters
- Identifying the Number of Clusters Demo
- Utilizing Clusters
- Lesson Introduction
- Feature Relationships
- Correlation Matrix
- Introduction to Principal Components Analysis
- Applied Principal Components Analysis
- PCA for Feature Relationships
- PCA for Dimensionality Reduction
Readings
- K-means Clustering Lab
- Principal Components Analysis Lab
Quizzes
- Exploring and Visualizing Data
- K-means Clustering
- K-means Clustering Lab Results
- Feature Correlation
- Principal Components Analysis
- PCA Lab Results
Discussion Prompt
- Clustering in Your World
Videos
- Lesson Introduction
- Introduction to Feature Engineering
- Common Feature Improvements
- Handling Missing Values
- Imputing Missing Values
- Feature Scaling
- Converting Feature Types
- Representing Categorical Features
- One-hot Encoding
- Lesson Introduction
- Problems with High Dimensions and Dimensionality Reduction
- A Review of Feature Importance
- Linear Regression Coefficients and P-values
- Introduction to Feature Selection
- Regularization
- Regularized Regression
- Applied Regularized Regression
Readings
- Feature Engineering Lab
- Feature Selection Lab
Quizzes
- Feature Engineering Concepts
- Missing Values
- Feature Engineering Lab Results
- Dimensionality and Feature Importance
- Feature Selection in Linear Regression
- Feature Selection Lab Results
Discussion Prompt
- Feature Correlation in Your World
Videos
- Lesson Introduction
- A Review of Decision Trees
- Algorithm Selection
- String Indexing Categorical Features
- Decision Tree Pruning
- Lesson Introduction
- Introduction to Ensemble Modeling
- Bootstrap Sampling Training Data
- Applied Random Forest
- Lesson Introduction
- A Review of Classification Evaluation Metrics
- A Review of Assigning Classes
- Oversampling and Undersampling Classes
- Weighting Classes in Random Forest
Readings
- Feature Engineering in Decision Trees
- Preventing Overfitting
- Applied Decision Trees Lab
- Aggregating Bootstrapped Results
- Random Forest Algorithm
- Applied Random Forest Lab
- Problems with Class Imbalance
- Label-based Bootstrap Sampling
- Label-based Evaluation Weighting
- Label Imbalance Lab
Quizzes
- Algorithm Selection and Decision Trees
- Categorical Features
- Applied Decision Trees Lab Results
- Tree-based Ensemble Modeling
- Bootstrap Aggregation
- Applied Random Forest Lab Results
- Classification Evaluation
- Label Imbalance and Sampling
- Label Imbalance Lab Results
Discussion Prompt
- Putting the Random in Random Forest
Videos
- Lesson Introduction
- Introduction to Hyperparameters
- Hyperparameters in Tree-based Models
- Optimizing Hyperparameters
- Grid Search for Hyperparameter Optimization
- Validation Set
- Grid-search for Random Forests
- Lesson Introduction
- A Review of Model Generalization
- Validation Set Limitations
- Introduction to Cross-Validation
- K-fold Cross-Validation with Random Forest
- Other Cross-Validation Strategies
Readings
- Hyperparameter Search Lab
- Cross-Validation Lab
Quizzes
- Hyperparameters in Tree-based Models
- Grid Search
- Hyperparameter Search Lab Results
- Model Generalization and Validation Set
- Cross-Validation
- Cross-Validation Lab Results
Instructors
Swiss Federal Institute of Technology Lausanne Frequently Asked Questions (FAQ's)
The online certification programme is the third and the last course in the Data Science with Databricks for Data Analysts Specialization.
The eligibility criteria to join the online programmes is that the learners must have completed two courses prior to this course in the specialization.
The faculties of the online course are Kevin Coyle, Mark Roepke, and Emma Freeman who work at Databricks.
Databricks, the data and AI company is the collaborator of Coursera to administer the course.
The online programme can be completed within about 16 hours.