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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 4,051
3 Months	INR 8,102
6 Months	INR 12,154

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

Machine learningData science knowledge

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

Big Data DeveloperBig Data Engineer

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)

Practice exercise

Course Introduction and Prerequisites

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

Practice exercises

Exploring and Visualizing Data
K-means Clustering
K-means Clustering Lab Results
Feature Correlation
Principal Components Analysis
PCA Lab Results

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

Practice exercises

Feature Engineering Concepts
Missing Values
Feature Engineering Lab Results
Dimensionality and Feature Importance
Feature Selection in Linear Regression
Feature Selection Lab Results

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

Practice exercises

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

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

Practice exercises

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)

1: In which specialization is the Applied Data Science for Data Analysts online course from?

The online certification programme is the third and the last course in the Data Science with Databricks for Data Analysts Specialization.

2: What are the eligibility criteria to join the Applied Data Science for Data Analysts online certification?

The eligibility criteria to join the online programmes is that the learners must have completed two courses prior to this course in the specialization.

3: Name the faculties who teach the online programme?

The faculties of the online course are Kevin Coyle, Mark Roepke, and Emma Freeman who work at Databricks.

4: Which company does Coursera join hands with to administer the online programme?

Databricks, the data and AI company is the collaborator of Coursera to administer the course.

5: How much time do the learners need to cover the online programme?

The online programme can be completed within about 16 hours.