CertNexus Certified Data Science Practitioner

Data Science jobs are the most popular job roles that stole all the limelight from the past 3 years. With such hype of hiring of data science professionals, the Certified Data Science Practitioner certification training offered by CertNexus is definitely a great addition to enhance employability prospects in future tech companies.

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CertNexus Certified Data Science Practitioner Course Description


Data Science professionals earn 25% more than average professional starting salaries. The field of Data Science has topped the Linked In Emerging Jobs list for the last 3 years with a projected growth of 28% annually and the World Economic Forum lists Data Analytics and Scientists as the top emerging job for 2022.


This course is designed for professionals who are wager to learn effective ways of extracting insights from given data and use those insights to address business issues. After attending this course, you will be able to validate your skills required to analyze, understand, manipulate, and present data within an effective and repeatable process framework and become a part of your organization’s robust workforce.
 

Course Curriculum


Audience

This course is designed for:

  • Business professionals who leverage data to address business issues.
  • A programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code.
  • A data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations.
  • Someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.
  • Students preparing for the CertNexus® Certified Data Science Practitioner (CDSP) (Exam DSP-110) certification.

Course Objectives

In this course, you will implement data science techniques in order to address business issues.

You will learn to:

  • Use data science principles to address business issues.
  • Apply the extract, transform, and load (ETL) process to prepare datasets.
  • Use multiple techniques to analyze data and extract valuable insights.
  • Design a machine learning approach to address business issues.
  • Train, tune, and evaluate classification models.
  • Train, tune, and evaluate regression and forecasting models.
  • Train, tune, and evaluate clustering models.
  • Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.

Eligibility Criteria

To ensure your success in this course, you should have:

  • At least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.
  • Experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.
  • Experience working with databases, including querying languages like SQL.

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Training Options


ONLINE /OFFLINE TRAINING

Online Live Interactive Training


  • Instructor-led Online Training
  • Experienced Subject Matter Experts
  • Training Material Available
  • 24*7 learner assistance and support

CORPORATE TRAINING

Customized According To Team's Requirements


  • Blended Learning Delivery Model (Self-Paced E-Learning And/Or Instructor-Led Options)
  • Course, Category, And All-Access Pricing
  • Enterprise-Class Learning Management System (LMS)
  • Enhanced Reporting For Individuals And Teams
  • 24x7 Teaching Assistance And Support 

Course Outline


Topic A: Initiate a Data Science Project

  • Data Science
  • Design Thinking
  • Project Scope
  • Scope Creep
  • Project Specifications and Objectives
  • Project Timeline
  • Project Deliverables
  • Project Stakeholders
  • Stakeholder Requirements
  • Proof of Concept (POC)
  • Minimum Viable Product (MVP)
  • Data Privacy and Security
  • Example Security Policies
  • Data Access
  • Data Governance
  • Guidelines for Initiating a Data Science Project
  • Initiating a Data Science Project

Topic B: Formulate a Data Science Problem

  • The Data Science Process
  • The Data Science Skillset
  • Shifting Skillsets
  • Problem Formulation
  • Common Issues Addressed by Data Science
  • Modeling Data
  • Data Science Outcomes
  • Learning Modes
  • Randomness and Uncertainty
  • Guidelines for Formulating a Data Science Problem
  • Formulating a Data Science Problem

Topic A: Extract Data

  • Datasets
  • Structure of Data
  • Terms Describing Portions of Data
  • Data Quantity
  • Big Data
  • Data Sources
  • Third-Party Data
  • Open Datasets
  • Public APIs
  • Public API Example
  • Extract, Transform, and Load (ETL)
  • Data Validation
  • Comma-Separated Values (CSV) Files
  • Data Read from CSV Files
  • Guidelines for Reading Data from CSV Files
  • Reading Data from CSV Files
  • SQL Queries
  • NoSQL Queries
  • Guidelines for Extracting Data with Database Queries
  • Extracting Data with Database Queries
  • Data Read from Cloud Storage
  • Data Consolidation
  • Data Joins
  • Inner Join Example
  • Guidelines for Consolidating Data from Multiple Sources
  • Consolidating Data from Multiple Sources

Topic B: Transform Data

  • Preliminary Data Transformation
  • Data Preparation and Cleaning
  • Types of Data
  • Operations You Can Perform on Different Types of Features
  • Continuous vs. Discrete Variables
  • Data Parsing
  • Guidelines for Parsing Data
  • Data Irregularities
  • Identification of Corrupted or Unusable Data
  • Guidelines for Handling Irregular and Unusable Data
  • Handling Irregular and Unusable Data
  • Correction of Data Formats
  • Date Conversion
  • Guidelines for Correcting Data Formats
  • Correcting Data Formats
  • Deduplication
  • Deduplication Without a Key
  • Deduplication of Columns
  • Guidelines for Deduplicating Data
  • Deduplicating Data
  • Word Embedding
  • Text Data Transformation Techniques
  • Image Data Representation
  • Guidelines for Transforming Data
  • Handling Textual Data

Topic C: Load Data

  • Data Loading Considerations
  • Data Loading: Databases
  • Guidelines for Loading Data into Databases
  • Loading Data into a Database
  • Data Loading: DataFrames
  • Guidelines for Loading Data into DataFrames
  • Loading Data into a DataFrame
  • Data Loading: Text Files
  • Guidelines for Loading Data into Text Files
  • Exporting Data to a CSV File
  • ETL Endpoints
  • Guidelines for Configuring ETL Endpoints
  • Data Loading: Visualization Tools
  • Guidelines for Loading Data into Visualization Tools
  • Exploring Data Visualization Tools

Topic A: Examine Data

  • Exploratory Data Analysis
  • Dataset Content and Format
  • Analysis of Feature Types
  • Target Features
  • Feature Relevance
  • Representative Data
  • Additional Sampling Techniques
  • Imbalanced Datasets
  • Errors, Outliers, and Noise
  • Correlations
  • Correlation Coefficient
  • Correlation Strength
  • Guidelines for Examining Data
  • Examining Data

Topic B: Explore the Underlying Distribution of Data

  • Frequency Distributions
  • Probability Distributions
  • Normal Distribution
  • Non-Normal Distributions
  • Descriptive Statistical Analysis
  • Central Tendency
  • When to Use Different Measures of Central Tendency
  • Variability
  • Range Measures
  • Variance and Standard Deviation
  • Calculation of Variance
  • Variance in a Sample Set
  • Calculation of Standard Deviation
  • Uses for Standard Deviation
  • Skewness
  • Calculation of Skewness
  • Kurtosis
  • Calculation of Kurtosis
  • Statistical Moments
  • Guidelines for Exploring the Underlying Distribution of Data
  • Exploring the Underlying Distribution of Data

Topic C: Use Visualizations to Analyze Data

  • Visualizations
  • Histograms
  • Guidelines for Analyzing Data Using Histograms
  • Analyzing Data Using Histograms
  • Box Plots
  • Violin Plots
  • Guidelines for Analyzing Data Using Box Plots and Violin Plots
  • Analyzing Data Using Box Plots and Violin Plots
  • Scatter Plots
  • Line Plots
  • Area Plots
  • Guidelines for Analyzing Data Using Scatter Plots, Line Plots, and Area Plots
  • Analyzing Data Using Scatter Plots and Line Plots
  • Bar Charts
  • Guidelines for Analyzing Data Using Bar Charts
  • Analyzing Data Using Bar Charts
  • Geographical Maps
  • Heatmaps
  • Guidelines for Analyzing Data Using Maps
  • Analyzing Data Using Maps
  • Plots in Combination (Bar Chart Grid)
  • Plots in Combination (Pair Plot)
  • Guidelines for Using Visualizations to Analyze Data
  • Comparing Visual Analysis Methods

Topic D: Preprocess Data

  • Data Analysis Prompts Further Transformation
  • Data Preprocessing
  • Identification of Missing Values
  • Imputation of Missing Values
  • Guidelines for Handling Missing Values
  • Handling Missing Values
  • Feature Scaling
  • Normalization and Standardization
  • Additional Transformation Functions
  • Guidelines for Applying Transformation Functions to Datasets
  • Applying Transformation Functions to a Dataset
  • Feature Engineering
  • Data Encoding
  • Data Encoding Methods
  • Guidelines for Encoding Data
  • Encoding Data
  • Continuous Variable Discretization
  • Bin Determination
  • Guidelines for Discretizing Variables
  • Discretizing Variables
  • Feature Splitting
  • Guidelines for Splitting Features
  • Splitting and Removing Features
  • Dimensionality Reduction
  • Dimensionality Reduction Methods
  • Guidelines for Performing Dimensionality Reduction
  • Performing Dimensionality Reduction
  • Guidelines for Preprocessing Data

Comparing Data Preprocessing Techniques

Topic A: Identify Machine Learning Concepts

  • Machine Learning
  • Machine Learning Models
  • Machine Learning Algorithms
  • Algorithm Selection
  • Iterative Tuning
  • Compromises
  • Bias and Variance
  • Model Generalization
  • The Bias–Variance Tradeoff
  • Holdout Method
  • Cross-Validation
  • Parameters
  • Guidelines for Training Machine Learning Models
  • Identifying Machine Learning Concepts

Topic B: Test a Hypothesis

  • Hypothesis
  • Design of Experiments
  • Hypothesis Testing
  • A/B Tests
  • A/B Tests and Machine Learning
  • Additional Hypothesis Testing Methods
  • p-value
  • Confidence Interval
  • Confidence Interval Visualization
  • Calculation of Confidence Interval
  • Guidelines for Testing a Hypothesis
  • Testing a Hypothesis

Topic A: Train and Tune Classification Models

  • Binary Classification
  • Logistic Regression
  • Decision Boundary
  • Multi-Label Classification
  • Multi-Class Classification
  • Multinomial Logistic Regression
  • Guidelines for Training Logistic Regression Models
  • Training a Logistic Regression Model
  • k-Nearest Neighbor (k-NN)
  • k Determination
  • Guidelines for Training k-NN Models
  • Training a k-NN Model
  • Support-Vector Machines (SVMs)
  • SVMs for Linear Classification
  • Hard-Margin Classification
  • Soft-Margin Classification
  • Guidelines for Training SVM Classification Models
  • Training an SVM Classification Model
  • Naïve Bayes
  • Naïve Bayes Example
  • Naïve Bayes Classification Characteristics
  • Guidelines for Training Naïve Bayes Models
  • Training a Naïve Bayes Model
  • Decision Tree
  • Classification and Regression Tree (CART)
  • Gini Index Example
  • Customer Retention Example Tree
  • CART Hyperparameters
  • Pruning
  • Ensemble Learning
  • Random Forest
  • Gradient Boosting
  • Gradient Boosting: Residuals
  • Gradient Boosting: Estimations
  • Guidelines for Training Classification Decision Trees and Ensemble Models
  • Training Classification Decision Trees and Ensemble Models
  • Hyperparameter Optimization
  • Grid Search
  • Randomized Search
  • Bayesian Optimization
  • Guidelines for Tuning Classification Models
  • Tuning Classification Models

Topic B: Evaluate Classification Models

  • Evaluation Metrics
  • Classification Model Performance
  • Considerations When Choosing Classification Metrics
  • Confusion Matrix
  • Accuracy
  • Precision
  • Recall
  • Precision–Recall Tradeoff
  • F1 Score
  • Specificity
  • Receiver Operating Characteristic (ROC) Curve
  • Thresholds
  • Area Under Curve (AUC)
  • Learning Curve
  • Guidelines for Evaluating Classification Models
  • Evaluating Classification Models

Topic A: Train and Tune Regression Models

  • Linear Regression
  • Linear Equation
  • Linear Equation Example Data
  • Straight Line Fit to Example Data
  • Linear Equation Shortcomings
  • Linear Regression in Machine Learning
  • Linear Regression in Machine Learning Example
  • Matrices in Linear Regression
  • Normal Equation
  • Guidelines for Training Linear Regression Models
  • Training a Linear Regression Model
  • Regression Using Decision Trees and Ensemble Models
  • Guidelines for Training Regression Trees and Ensemble Models
  • Training Regression Trees and Ensemble Models
  • Forecasting
  • Autoregressive Integrated Moving Average (ARIMA)
  • ARIMA Temperature Example
  • Guidelines for Training Forecasting Models
  • Cost Function
  • Regularization
  • Regularization Techniques
  • Gradient Descent
  • Learning Rate
  • Grid/Randomized Search for Regression
  • Guidelines for Tuning Regression Models
  • Tuning Regression Models

Topic B: Evaluate Regression Models

  • Cost Functions Used to Evaluate Regression Models
  • Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)
  • Coefficient of Determination
  • Guidelines for Evaluating Regression Models
  • Evaluating Regression Models

Topic A: Train and Tune Clustering Models

  • k-Means Clustering
  • Global vs. Local Optimization
  • k Determination
  • Guidelines for Training k-Means Clustering Models
  • Training a k-Means Clustering Model
  • k-Means Clustering Shortcomings
  • Hierarchical Clustering
  • Hierarchical Clustering Applied to a Spiral Dataset
  • Guidelines for Training Hierarchical Clustering Models
  • Training a Hierarchical Clustering Model
  • Latent Class Analysis
  • Clustering Hyperparameters and Tuning
  • Guidelines for Tuning Clustering Models
  • Tuning Clustering Models

Topic B: Evaluate Clustering Models

  • Evaluation Metrics for Clustering
  • Elbow Point
  • Cluster Sum of Squares
  • Silhouette Analysis
  • When to Stop Hierarchical Clustering
  • Dendrogram
  • Guidelines for Evaluating Clustering Models
  • Evaluating Clustering Models

Topic A: Communicate Results to Stakeholders

  • Know Your Audience
  • Derive Insights from Findings
  • Explainability
  • Global vs. Local Interpretability
  • Additional Factors That Drive Outcomes
  • Present Model Results
  • Use Visuals in Presentations
  • Dashboards
  • Cumulative Gains Charts
  • Lift Charts
  • Guidelines for Communicating Results to Stakeholders
  • Communicating Results to Stakeholders

Topic B: Demonstrate Models in a Web App

  • Web App
  • HTML
  • CSS
  • JavaScript
  • Web Frameworks
  • Common Web Frameworks
  • Flask
  • Django
  • Guidelines for Demonstrating Models in a Web App
  • Demonstrating Models in a Web App

Topic C: Implement and Test Production Pipelines

  • Put a Model into Production
  • Data Pipelines
  • Model Drift
  • Docker
  • Kubernetes
  • Amazon SageMaker
  • Azure Machine Learning
  • Monitor Models in Production
  • Pipeline Monitoring Solutions
  • Guidelines for Implementing and Testing Production Pipelines
  • Building an ML Pipeline

Course Reviews


FAQ's


Vinsys is a leading provider of quality professional trainings that are focused on creating a skillful workforce across the globe to serve the future tech companies. With over two decades of training expertise and a robust approach towards learning methods, we strive to offer more than expected to our students. CertNexus courses are gaining momentum and we are proud to offer emerging technology trainings to our students. Our highly professional, industry-expert trainers ensure practical skill enhancement while also focusing on concept clarity during sessions. We are recognized and appreciated for our training efforts and results by all of our students.

Data science is getting all the attention when it comes to the job market. Professionally certified data scientists are likely to get flat 25% hike in starting salaries as reported by LinkedIn. So, this course is definitely going to offer you immense opportunities in the data-oriented corporate industry. CertNexus is one of the leading providers of emerging tech certifications and taking this course from Vinsys gives you an added advantage.

Candidates applying for this course are recommended to have:

  • At least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.
  • Experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.
  • Experience working with databases, including querying languages like SQL.

The exam will certify that the successful candidate has the knowledge, skills, and abilities required to answer questions by collecting, wrangling, and exploring data sets, applying statistical models and artificial-intelligence algorithms, to extract and communicate knowledge and insights.

 

Exam Code: DSP-110

Format: Multiple choice/Multiple response

Duration: 120 minutes (including 5 minutes for Candidate Agreement and 5 minutes for Pearson VUE tutorial)

Passing Score: 70%