The “IBM Data Science Professional Certificate” offered on St Bit is a comprehensive program designed to equip learners with the essential skills and knowledge in data science. Covering a wide range of topics such as data analysis, machine learning, Python programming, and data visualization, this certificate program prepares individuals for a career in the data science field.
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.
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
Create and manage source code for data science using Git repositories and GitHub.
Describe what a 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.
Determine an appropriate analytic model including predictive, descriptive, and classification models to analyze a case study.
Determine appropriate sources of data for your data science project.
Describe Python Basics including Data Types, Expressions, Variables, and Data Structures.
Apply Python programming logic using Branching, Loops, Functions, Objects & Classes.
Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.
Access web data using APIs and web scraping from Python in Jupyter Notebooks.
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, Python data structures, and working with data in Python.
Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.
Analyze data within a database using SQL and Python.
Create a relational database on Cloud and work with tables.
Construct SQL statements including SELECT, INSERT, UPDATE, and DELETE.
Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures and joins.
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-word datasets using libraries such as Pandas, Numpy and Scipy
Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making
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 line, area, histograms, bar, pie, box, 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
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 metrics
Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders
Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation
Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors
Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model
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