The “Certified Data Science” course lets you gain proficiency in Data Science. It is a high-level data science notion designed aiming to cover all the aspects of data science with core concepts. Data Science is one of the most happening fields in business today, creating a higher number of career opportunities. The certification is  IABAC (International Association of Business and Analytics Certification) accredited which is a global certification. The course has inclusive realms, namely Statistics, Machine Learning/ Programing/Data Skills, Business Domain knowledge; covering all the mains of the Data Science helps you to achieve a solid grip over it.

• These four facets form four pillars for the data science field. They are 1. Programing 2. Statistics 3. Machine Learning 4. Business Knowledge.
• The course is mainly focussed on Python for core data science programming, it also includes R as necessary to enable professionals working in R.
• Statistics are covered as required for a Data Scientist, you may find a detailed syllabus in the syllabus tab.
• Machine Learning is the main tool kit for Data Science in predicting classification or regression.
• This course courses all popular ML algorithms as detailed in the syllabus tab.
• This course allows candidates to obtain in-depth knowledge by laying a strong foundation and covering all the latest data science topics.
• The increasing demand curve for data science professionals to manage the large set of data in various organizations providing millions of job opportunities in global markets.
• The knowledge gained through this course along with IABAC™ certificate surely helps you to become a data science professional.

Course Module

Data Science Foundation

• Introduction To Data Science
• Industry Applications
• Terminologies

Python Essentials

• Anaconda – Python Distribution Installation And Setup
• Jupyter Notebook
• Python Basics
• Data Structures
• Control Statements

R Language Essentials

• R Installation And Setup
• R Studio Basics
• R Data Structures
• Control Statements
• Data Science Packages

Maths For Data Science

• Essential Mathematics
• Linear Algebra
• Linear Transformation
• Types Of Matrices, Matrix Properties, And Operations Probability And Calculus

Statistics For Data Science

• Statistics Introduction
• Terminologies
• Inferential Statistics
• Harnessing Data
• Exploratory Analysis
• Distributions
• Central Limit Theorem
• Hypothesis Testing
• Correlation, And Regression

Data Preparation With Pandas

• Numpy Array Functions
• Data Munging With Pandas
• Imputation
• Outlier Analysis

Visualization With Python

• Visualization Basics
• Matplotlib Introduction
• Basic Plots
• Customizing Plots
• Sub-Plots
• Statistical Plots
• Seaborn Package Introduction

Machine Learning Associate

• Machine Learning Introduction
• Ml Core Concepts
• Unsupervised And Supervised Learning
• Clustering With K-Means
• Linear Regression
• Logistic Regression
• K-Nearest Neighbor

Advanced Machine Learning

• Bayes Theorem
• Naïve Bayes Algorithm For Text Classification,
• Decision Tree
• Ensemble Methods: Random Forest,
• Extra Trees
• Svm, Boosting Techniques
• Xgboost
• Artificial Neural Network
• Adv Metrics
• Imbalanced Dataset
• Grid Search
• K-Fold Cross-Validation

Sql For Data Science

• Relational Database Management Systems Basics
• Sql Introduction
• Connection To Sql Databases
• Fetching Data With Select
• Where Condition
• Sql Joins
• Sql Crud Operations

Deep Learning – Cnn Basics

• Deep Learning Introduction
• Tensorflow And Keras
• Convolution Neural Network Basics
• End To End Image Classification Of Cats And Dogs Using The Tensorflow-Keras Platform.

Tableau Associate

• Visual Analytics Basics
• Tableau Introduction
• Connecting To Datasource
• Dimensions Vs Measures
• Basic Plots
• Compound Plots
• Forecasting
• Publishing

Ml Model Deploy- Flask Api

• Ml Deployment Strategies
• Flask Introduction
• Packing Training Ml Model
• Deploying It On Flask As Api

Data Science Project Execution

• Data Science Project Management Method
• Business Case Risk
• Limitation Of Machine Learning
• Project Pitfalls.

Big Data Foundation

Introduction to Big Data

• Hadoop Concepts
• Spark Big Data for Data Science Processing
• Handling Big Data in Machine Learning Pipeline.