Data Science Fundamentals Practice Exam
Data Science Fundamentals Practice Exam
About the Data Science Fundamentals Exam
Data Science Fundamentals cover the essential concepts and techniques used to analyze and interpret data. It involves understanding data cleaning, exploration, and visualization, as well as applying statistical analysis and machine learning algorithms to derive insights. Key skills include proficiency in programming languages like Python and R, familiarity with data manipulation libraries (e.g., Pandas, NumPy), and the ability to work with databases and big data tools. A solid foundation in data science also includes knowledge of probability, linear algebra, and model evaluation techniques, enabling effective problem-solving in various industries.
Skills Required
- Basic programming skills in Python or R.
- Strong foundation in mathematics and statistics, including probability, linear algebra, and calculus.
- Familiarity with data manipulation and analysis tools like Pandas, NumPy, and SQL.
- Knowledge of data visualization tools like Matplotlib, Seaborn, or Tableau.
- Understanding of basic machine learning concepts and algorithms, such as regression, classification, and clustering.
- Experience with databases (SQL and NoSQL) and big data tools like Hadoop or Spark.
- Problem-solving mindset with a curiosity to explore data patterns and relationships.
Knowledge Gained
In this course you will gain:
- Understanding of core data science concepts, including data cleaning, exploration, and visualization.
- Proficiency in programming languages like Python and R for data analysis and modeling.
- Knowledge of key data manipulation libraries such as Pandas, NumPy, and SQL for handling datasets.
- Ability to apply statistical analysis and machine learning algorithms to draw insights from data.
- Skills in visualizing data using tools like Matplotlib, Seaborn, or Tableau to identify trends and patterns.
- Hands-on experience with supervised and unsupervised learning models for predictive analytics.
- Understanding of database management and big data technologies for working with large-scale datasets.
- Strong problem-solving and analytical thinking skills to approach real-world data challenges.
Who should take the Exam?
- Aspiring data scientists looking to build a career in data science and analytics.
- Machine learning engineers aiming to deepen their understanding of data science fundamentals.
- Data analysts wanting to transition to data science or enhance their analytical capabilities.
- Software developers interested in expanding their skill set into data science and machine learning.
- Business analysts seeking to leverage data science for better decision-making and insights.
- AI/ML enthusiasts eager to validate their skills with a formal exam.
- Career changers looking to switch to the data science field and gain foundational knowledge.
Course Outline
Welcome and Logistics
- Introduction and Outline
- Course Resources
NumPy
- NumPy Section Introduction
- Arrays Versus Lists
- Dot Product
- Speed Test
- Matrices
- Solving Linear Systems
- Generating Data
- NumPy Exercise
- Where to Learn More NumPy
- Suggestion Box
Matplotlib
- Matplotlib Section Introduction
- Line Chart
- Scatterplot
- Histogram
- Plotting Images
- Matplotlib Exercise
- Where to Learn More Matplotlib
Pandas
- Pandas Section Introduction
- Loading in Data
- Selecting Rows and Columns
- The apply() Function
- Plotting with Pandas
- Pandas Exercise
- Where to Learn More Pandas
SciPy
- SciPy Section Introduction
- PDF and CDF
- Convolution
- SciPy Exercise
- Where to Learn More SciPy
Machine Learning Basics
- Machine Learning: Section Introduction
- What Is Classification?
- Classification in Code
- What Is Regression?
- Regression in Code
- What Is a Feature Vector?
- Machine Learning Is Nothing but Geometry.
- All Data Is the Same
- Comparing Different Machine Learning Models
- Machine Learning and Deep Learning: Future Topics
- Machine Learning: Section Summary
