Is the IBM Data Science Certificate Worth It?
Table of Content
- What is Data Science?
- Tools for Data Science.
- Data Science Methodology.
- Python for Data Science, Artificial Intelligence, and Development.
- Python Project for Data Science.
- Database and SQL for Data Science With Python.
- Data Analysis With Python.
- Data Visualization With Python.
- Machine Learning With Python.
- Applied Data Science Capstone.
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The IBM data science certificate is a highly well-liked data science credential. For every sort of certification, the same criteria may be used to assess the quality of the IBM data science certificate. We can, however, state that the IBM data science certificate is appropriate for beginning students who desire to earn this credential and begin working as data scientists.
The worth of any certificate depends upon the following factors:
- The teacher should be well versed in the field of data science.
- The course’s review rating should be high if it determined its popularity level; if above 4.5, it is considered an excellent one.
- The sequence, of course, should be completing the learning dots.
- There should be an explicit mentioning of the scope of the course.
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The IBM Watson platform is used in this IBM data science course. We can claim that this course is strong at the foundational level and that it is ideal for students at the beginning level.
This course is being delivered through a number of third-party sites.
One of the most well-known credentials worldwide is the IBM data science certificate.
The key characteristics of the IBM data science certificate are listed below so that readers may assess its value.
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Defining Data Science and What Data Scientists Do
The IBM data science certificate programme in conjunction with Coursera offers a ten-module data science course. Each module's value may be assessed based on the information and practical experience it provides. Each module's specifics are listed. By examining the course material, we can impartially assess the value of the course.
The name of the first module is What Is data science?
What is Data Science?
This course comprises 10 hours, and it is divided into three parts which are stated below.
Defining data science and what data scientist do
In this course, we will study what data science is and what data scientists do, and we will discuss the tools data scientists use.
Data science topics
In this module, we will study the topics of the data sciences.
Data science in business
This module will discuss the application of data sciences in the business world.
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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.
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.
Python Programming Fundamentals
This module explains the Python programming language's foundational concepts. In this subject, the ideas of conditions and branching are further discussed. This module also describes how to insert loops.
Working with Data in Python
How to read and write it is stated in this module. In addition to it, data manipulation and operation are also expressed in it.
Python Project for Data Science
We will be able to use our essential Python abilities for working with data after completing this course. This course will include practical assignments that will improve our practical abilities.
- We will also learn HTML for web scraping
- In addition to it, we will know Stock shares
- We will study GameStop stock versus Tesla
- You will also learn Jupyter Notebook.
- Two practice exercises
- Extracting stock data using Python library.
- In this course, we will learn crowdsourcing short squeeze dashboard.
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.
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.
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
We are importing and exporting a database with python in this module.
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.
This module deals with linear regression and multi multiple regression, model evaluation using visualization, polynomial regression, and pipelines, Maihar for In-sample Evaluation.
This module deals with Model valuation and refinement, overfitting, underfitting, and Ridge regression.
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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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.
This module will teach us hands-on practice using the final project.
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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.
This model will learn about linear, nonlinear, multiple, and simple regression.
This module will learn different classification algorithms, Logistic regression, KNN, decision trees, and SVM.
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.
In this module, we will learn about two main types of recommendation systems that are, content-based and collaborative filtering systems.
The project will be given an assignment from our whole course, and we will be judged by peer-review evaluation.
Applied Data Science Capstone
In this module, we will learn about location data and different location data providers, and what location is generally composed of.
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.
The components of the IBM data science course are these 10 modules. We can see that this course has a lot of information, and tasks are also discussed. This course is more than a foundational course; it will assist a student in gaining practical data science expertise.
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