The demand for qualified and well-skilled data scientist professionals has seen significant growth over the past few years and that will continue to be the case for the foreseeable future majorly because of the scale of data available and the amount that is generated every second, companies and organization out there are in search for a well capable skilled data scientist to derive insight from them.
For those interested in starting a new career in data science or who want to switch to the data science field then this course is for you. the specialization is beginner friendly and no prior knowledge is required since every skill needed is thought with other learning materials included. Another point that makes it stand out is it’s taught in python, as python is the preferred programming language by industry professionals and most employers prefer python programmers and in some cases are paid more than other programmers.
The IBM Data Science Professional consists of ten courses which include a final capstone project and is completely online, flexible and subtitled in multiple languages with an estimated eleven months completion period, you can also enrol for each course individually and you will receive an IBM digital badge and certificate upon completion. A brief overview of the specialization is:
What is Data Science
This chapter covers the introductory part, the basis and interviews data scientists from various industries, it defined who a data scientist is, a day in the life of a data scientist, what they do and the characteristic of a good data scientist. it further goes into concepts of data science with some hands-on quizzes.
Tools for Data Science
When it comes to tools for data science they’re a lot of them and this chapter highlights and emphasizes the most important and relevant tools, you get an overview of the programming languages commonly used like python, R, Scala and SQL, an introduction to opensource like git, GitHub, jupyter notebook, jupyterlab, RStudio IDE, you'll also learn about other IBM tools used to support data science projects, such as IBM Watson knowledge Catalogue, Data Refinery and the SPSS Modular.
Data Science Methodology
This chapter takes us through the process and steps that are taken to complete a data science task successfully including from data acquisition to model deployment, this is very important because in data science we need to apply a certain framework to guide us in problem-solving. the data science methodology framework includes:
an iterative process with a prescribed sequence of steps that are followed by data scientists to approach a problem and find a solution. It is a cyclic process that guides business analysts and data scientists to perform suitably.
in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practising data science, from forming a concrete business or research problem to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
Python for Data Science, AI & Development
This course dives deep into the technical aspect of the specialization, you will gradually gain proficiency in python with the use of jupyter notebook in a matter of hours. You will cover the basics of python and apply useful python libraries in this course.
Python Project for Data Science
This is the shortest course in this specialization with just a week duration, in this project you will be working as a data scientist with the objective to extract a stock price from tesla and game stop stock. This mini-course is intended for you to demonstrate foundational Python skills for working with data. The completion of this course involves working on a hands-on project where you will develop a simple dashboard using Python.
Databases and SQL for Data Science with Python
Working with SQL, SQL (or Structured Query Language) is such a powerful language which is used for communicating with and extracting data from databases. No prior knowledge of SQL is needed as it will be covered in this course and in the hands-on assignment you will connect your jupyter notebook to a database using an API
Data Analysis with Python
You will learn how to analyse data using python, and how to use functions in pandas and NumPy libraries. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!
Here you will learn how to:
1) Importing Datasets
2) Cleaning the Data
3) Data frame manipulation
4) Summarizing the Data
5) Building machine learning Regression models
6) Building data pipelines
the important thing is to take data from its raw or unstructured form and create a visualization
Data Visualization with Python
In this course, the objective is to use pictures in form of graphs to the representation of both small and large-scale data. to present the data meaningful to individuals, to accomplish that, we will use the appropriate library, matplotlib, seaborn and folium are used to accomplish that in this course to plot charts like pie charts, boxplots, histograms, area plots, scatter plots and some advanced visualizations like the waffle charts and word clouds, learning how to create a dashboard using plotly.
Machine Learning with Python
This course covers the basics of machine learning with real-life examples and the role machine learning play in our daily life. you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms such as regression, classification, clustering, sci-kit learn and SciPy including some hands-on projects like cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and much more you can add to your portfolio
Applied Data Science Capstone
The final course is more of a project, you will apply the knowledge you've earned to solve some practical problems. in this part, you will assume the role of a data scientist working for a start-up and going up against Elon Musk’s SpaceX. You will solve a series of real-life problems. you can always refer back to the videos in these courses for a quick refresh and after successful completion, you will be awarded a certificate in Data Science to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.
Conclusion
The IBM data scientist is definitely worth it, after close inspection of the curriculum, they are still relevant in the industry today, it includes what anyone needs to start a career or to switch to a career in tech in 2022. in a challenging job market, the IBM data science certificate program can help you stand out and land you your next role, with the knowledge you will acquire from this specialization, you can apply to help companies succeed in this exciting field
