IBM Data Science Professional Specialization - Highlights & Insights

Data science is a relatively new field that is becoming increasingly popular, especially in the business world. As an experienced business manager, I have found that data science can be used to improve various aspects of businesses, from sales and marketing to business development and investment management.

I completed the specialization because I wanted to gain some practical skills in data science, and think and act as Data Scientist. It was a tough journey, but it was worth it!

The specialization courses provided the skills and knowledge needed to become a data scientist. The courses cover a wide range of topics, from data mining and machine learning to data visualization and communication. It was great learning how to use data to solve real-world problems and how to communicate the findings to non-technical audiences. One of the things I liked most about this specialization was the focus on practical skills. At the end of the specialization, I completed a real data science project (Applied Data Science Capstone).

In the capstone, I demonstrated proficiency in data science and machine learning techniques using a real-world data set. I have written Python code to create machine learning models, including support vector machines, decision tree classifiers, and k-nearest neighbors. I enjoyed performing data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation, I evaluated the results of machine learning models for predictive analysis, compared their strengths and weaknesses, and identified the optimal model.?

What was the capstone about? I was excited?to analyze SpaceX launch data, synthesizing my machine-learning models, data frames, and queries into a final project presentation.

Sounds interesting? Here is what you can learn.

The first course in the specialization is What is Data Science? It explains why Data Science has been labeled as one of the hottest professions of the 21st century.

The Second course in the specialization is Tools for Data Science. This course provides hands-on experience, you will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as various Open source, commercial, Big Data, and Cloud-based tools. you will also work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio.

The third course in the specialization is Data Science Methodology. This course will learn how to think like a Data Scientist. I will teach you how to apply the methodology that can be used to tackle any Data Science scenario, working on real-world data with Jupyter Notebooks. You will learn how to form a business/research problem, collect, prepare & analyze data, build a model, deploy a model and understand the importance of feedback. You will also apply the 6 stages of the CRISP-DM methodology, the most popular methodology for Data Science and Data Mining problems.

The fourth course in the specialization is Python for Data Science, Artificial Intelligence & Development. In this course, you will learn about Python for data science, as well as programming in general. Python is one of the world’s most popular programming languages. driving business solutions across industries.

The third course in the specialization is Python for Data Science. In this course, you will learn about the basics of Python programming. You will also learn about different libraries that are used for data science, such as Pandas and Numpy.

The fourth course in the specialization is R for Data Science. In this course, you will learn about the basics of the R programming language. You will also learn about different libraries that are used for data science.

The fifth course is Python Project for Data Science. In this course, you will demonstrate foundational Python skills for working with data by working on a hands-on project where you will exercise and develop a simple dashboard using Python.

The sixth course in the specialization is Databases and SQL for Data Science with Python.SQL (or Structured Query Language) is a must-have for data Data Scientists. You will learn SQL from Select statements to advanced concepts like JOINs. You will analyze multiple real-world datasets to demonstrate your skills in hands-on labs and projects.

The seventh course in the specialization is Data Analysis with Python. In this course, you will learn the basics of data analysis with Python to build and evaluate data models. Topics covered include: collecting and importing data from various sources, cleaning, preparing & formatting data, data frame manipulation, summarizing data, building machine learning regression models, model refinement, and creating evaluation data pipelines.

The eighth Data course is Visualization with Python. It is designed to help you learn how to use the Python programming language to create stunning visualizations to better understand data. You'll learn how to use the popular matplotlib library to create both static and interactive visualizations, and you'll also explore how to use seaborn, a Python library for creating statistical visualizations. By the end of this course, you'll have a strong foundation in Python visualization and will be able to create your own visualizations to better understand data.

The ninth course is Machine Learning with Python. In this specialization, you will learn about the basic principles of machine learning as well as how to apply them in Python. You will also learn about some of the most popular machine learning algorithms, including linear regression, support vector machines, and decision trees. By the end of the specialization, you will be able to build and evaluate machine learning models in Python.

The tenth and final course in the specialization is Applied Data Science Capstone. In this project, you will apply the skills you have learned in the previous courses to a real-world data science problem.

After completing the IBM Data Science Professional Specialization, you will have the skills to become a data science professional. I can say that the journey with this specialization?was a fantastic opportunity for?learning new technologies and hands-on experience - I really liked the focus on practical skills.

The courses are all designed to give you hands-on experience with data science tools and techniques, and you get to practice your skills on real-world data sets. This is invaluable for learning how to become a data scientist and applying data science to solve real-world problems.

In today's business world, understanding data is a critical success factor. Those who know how to harness its power will be the ones who have better chances to succeed. As a seasoned business developer and growth expert, I learned data science to gain a competitive edge. The journey was very challenging yet rewarding.

IBM Data Science Professional Specialization - Certificate

IBM Data Science Professional Specialization - Badge