Is the IBM Data Science Certificate Worth It?

Tools for Data Science

This course comprises 22 hours, and it is divided into three parts: stated below.

• Data Scientist’s tool kit

  This module will get information about the programming of data sciences, including python, R, scapula, and SQL.

• Open data source

  This module will get information about open sources like GitHub, jupyter notebook, R Studio IDE.

• IBM Tools for data science

  This module will get information about IBM Platform called Watson Studio and information about SPSS Modeler.

Data Science Methodology

This course comprises 10 hours, and it is divided into three parts which are stated below.

• From problem to approach and from requirements to collection

  This module will understand the problem and the analytical approach to solve the problem.

• From understanding to preparation and from modeling to Evaluation

  This module will understand the process from the preparation of data until evaluating the result.

• From deployment to feedback

  This module will solve a peer review assignment to understand our learning.

Python for Data Science, Artificial Intelligence, and Development

This course comprises 14 hours, and it is divided into three parts: stated below.

• Python Basics

  This module will teach the different types of integers and variables and the necessary knowledge about python.

• Python Data Structures

  This module will teach the basics of data structures and how data is stored and recorded in python.

Database and SQL for Data Science with Python

This 14-hour course is broken up into the three sections listed below. A data scientist has to have a working understanding of SQL and database management systems.

• Getting Started with SQL

  Orientation Classes of SQL are Given in this Module.

• Introduction to Relational Databases and Tables

  Concepts of relationships and tables are mentioned in this module, and live dashboard access is available.

• Intermediate SQL

  Basic string patterns have Rangers to search data and sort data in a result set given in this module.

• Accessing Databases using Python

  Basic concepts of the database using python are given in this module. In jupyter Notebook, we will create tables, load data, and analyze data using python.

• Course Assignment

  Payment we will be working on the real-world data.

• Bonus Module: advanced SQL for data engineers

  This is a bonus module, and it is not compulsory, but if you are a data engineer and must do this course as it will be your profile, you can build the most potent queries with advanced SQL techniques like views, transactions, stored procedures, etc.

  There are four practice exercises in this module.

Data Analysis with Python

• Importing Database

  We are importing and exporting a database with python in this module.

• Data Wrangling

  This module deals with python's missing values, data normalization in Python, and binning in python.

• Exploratory Data Analysis

  This module deals with Python’s exploratory data analysis, descriptive analysis, Correlation, Association with Categorical variables.

• Model Development

  This module deals with linear regression and multi multiple regression, model evaluation using visualization, polynomial regression, and pipelines, Maihar for In-sample Evaluation.

• Model Evaluation

  This module deals with Model valuation and refinement, overfitting, underfitting, and Ridge regression.

• Final Assignment

  This module deals with the case-based scenario.

• IBM Digital Badge

  IBM digital badge will be given after completion of this module.

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Applied Data Science Capstone

• Introduction

  In this module, we will learn about location data and different location data providers, and what location is generally composed of.

• Foursquare API

  This module will learn in detail about the four-square API, which is the location service provider. We will also learn about how to create a Foursquare API account. Moreover, how to find the desired location service provider.

• Neighborhood Segmentation and Clustering

  This module will learn about k-means clustering, a form of unsupervised clustering. You will also learn how to scrap data and use it Panda data frame.

• The Battle of Neighborhoods

  This module will learn about the data and how it will help us solve the problem.

• The Battle of Neighborhoods

  We will solve a peer review assignment for the battle of neighborhoods.

Data Visualization With Python

This course comprises 17 hours, and it is divided into three parts: stated below.

• Introduction to the Data Visualization Tool

  This module will help you learn about the history and architecture of matplotlibband, How to use CSV files into Pandas Dataframe.

• Necessary and Specialized Visualization Tools

  This module will help you learn about area plots, histograms, Pie Charts, bar charts, etc.

• Advanced Visualization and Geospatial Data

  This module will teach about the waffle charts and word clouds, and we will also learn about seaborn, another visualization library. This module is designed for visualizing geospatial data.

• Creating Dashboards With Portly and Dash

  This module will teach about dashboard creation using Plotly library and dash.

• Final Project

  This module will teach us hands-on practice using the final project.

Machine Learning With Python

There are two parts to this course. We will first learn about the primary goal of machine learning and its practical applications. Second, we will study the specifics of supervised and unsupervised learning, as well as several machine learning techniques.

• Introduction to Machine Learning

  We will learn different areas in which machine learning is appropriate and different unsupervised vs. supervised learning python on machine learning.

• Regression

  This model will learn about linear, nonlinear, multiple, and simple regression.

• Classification

  This module will learn different classification algorithms, Logistic regression, KNN, decision trees, and SVM.

• Clustering

  In this module, we will learn about different segments of customers, and we will learn about three types of clustering, including partition-based clustering, density-based clustering, and hierarchical clustering.

• Recommender Systems

  In this module, we will learn about two main types of recommendation systems that are, content-based and collaborative filtering systems.

• Final Project

  The project will be given an assignment from our whole course, and we will be judged by peer-review evaluation.