The field of data science is rapidly evolving, and staying ahead of the curve is crucial for success. Free IBM Courses offers a comprehensive and well-respected platform to hone your data science skills and propel your career forward. This guide explores the top 10 Free IBM Courses specifically designed for data scientists, catering to various experience levels and specific skill sets.
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About the IBM
The International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and is present in over 175 countries. IBM is the largest industrial research organization in the world, with 19 research facilities across a dozen countries, having held the record for most annual U.S. patents generated by a business for 29 consecutive years from 1993 to 2021.
IBM was founded in 1911 as the Computing-Tabulating-Recording Company (CTR), a holding company of manufacturers of record-keeping and measuring systems. It was renamed “International Business Machines” in 1924 and soon became the leading manufacturer of punch-card tabulating systems. During the 1960s and 1970s, the IBM mainframe, exemplified by the System/360, was the world’s dominant computing platform, with the company producing 80 per cent of computers in the U.S. and 70 per cent of computers worldwide.
What is Data Scientists?
Data scientists are a blend of statisticians, computer scientists, and business analysts. They use their knowledge of math, statistics, coding, and data analysis to extract valuable insights from large and complex datasets.
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Here are the Top 10 Best Free IBM Courses For Data Scientists
1. What is Data Science?
What you’ll learn?
Define data science and its importance in today’s data-driven world.
Describe the various paths that can lead to a career in data science.
Summarize advice given by seasoned data science professionals to data scientists who are just starting out.
Explain why data science is considered the most in-demand job in the 21st century.
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2. Python for Data Science, AI & Development
What you’ll learn?
Learn Python – the most popular programming language and for Data Science and Software Development.
Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.
Access and web scrape data using APIs and Python libraries like Beautiful Soup.
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3. Tools for Data Science
What you’ll learn?
Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Utilize languages commonly used by data scientists like Python, R, and SQL
Demonstrate working knowledge of tools such as Jupyter Notebooks and RStudio and utilize their various features Â
Create and manage source code for data science using Git repositories and GitHub.Â
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4. Databases and SQL for Data Science with Python
What you’ll learn?
Analyze data within a database using SQL and Python.
Create a relational database and work with multiple tables using DDL commands.
Construct basic to intermediate-level SQL queries using DML commands.
Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.
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5. Machine Learning with Python
What you’ll learn?
Describe the various types of Machine Learning algorithms and when to use them
Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression
Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees
Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metricsÂ
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6. Data Science Methodology
What you’ll learn?
Describe what a data science methodology is and why data scientists need a methodology.
Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
Determine appropriate data sources for your data science analysis methodology
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7. Data Visualization with Python
What you’ll learn?
Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story
Create different types of charts and plots such as lines, areas, histograms, bars, pie, boxes, scatter, and bubble
Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps
Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library
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8. Data Analysis with Python
What you’ll learn?
Develop Python code for cleaning and preparing data for analysis – including handling missing values, formatting, normalizing, and binning data
Perform exploratory data analysis and apply analytical techniques to real-world datasets using libraries such as Pandas, Numpy and Scipy
Manipulate data using data frames, summarize data, understand data distribution, perform correlation and create data pipelines
Build and evaluate regression models using the machine learning sci-kit-learn library and use them for prediction and decision-making
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9. IBM Data Science Professional Certificate
What you’ll learn?
Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL
Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines
Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers
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10. Python Project for Data Science
What you’ll learn?
Play the role of a Data Scientist / Data Analyst working on a real project.
Demonstrate your Skills in Python – the language of choice for Data Science and Data Analysis.
Apply Python fundamentals, and Python data structures, and work with data in Python.
Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter Notebook.
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How To Enroll This Free Courses?
Follow this Step to Enroll on This Free Course:
1. Choose a course For you type
2. Then Sign Up to Coursera.
3.Chose a Program Then Click On the Next Option
4. Then Show an Interface and Click On the AUDIT Option this course is free.
5. Then Start The Course
