Welcome to the “Data Science Fundamentals” course by Lavanya Vijayan! In this course, you will learn the basics of data science, including key concepts, techniques, and tools used by data scientists to extract insights from data. Whether you are new to data science or have some experience with it, this course will provide you with a strong foundation to build your skills and knowledge in this field. Through a combination of lectures, hands-on exercises, and real-world examples, you will gain a practical understanding of data science and how it can be used to solve real-world problems. So, let’s dive in and explore the fascinating world of data science together!
Here is what you will learn:
- The difference between data scientists, data engineers, statisticians, and business analysts:
- Data Scientists: Data scientists are responsible for analyzing and interpreting complex data using statistical and computational techniques. They are skilled in programming languages like Python and R and are able to extract insights from large and complex data sets.
- Data Engineers: Data engineers are responsible for the design, construction, maintenance, and optimization of an organization’s data architecture. They are skilled in creating and maintaining data pipelines, databases, and other data-related infrastructures that support data analysis.
- Statisticians: Statisticians are experts in applying mathematical and statistical methods to collect, analyze, and interpret data. They focus on creating models and developing new statistical methods to answer research questions and solve problems.
- Business Analysts: Business analysts work with data to identify trends and insights that can inform business decisions. They use statistical methods to analyze data and make recommendations based on their findings.
- About the impact of data science on business and industry: Data science has transformed the way businesses operate in many industries. With the ability to collect and analyze large amounts of data, organizations can now make data-driven decisions and gain insights that were previously impossible. Data science has been used to improve customer experiences, optimize operations, reduce costs, and increase revenue.
- The workings of each stage of the data science life cycle including formulating a question, acquiring and cleaning data, conducting exploratory analyses, and drawing conclusions: The data science life cycle consists of several stages, including:
- Formulating a Question: Defining the research question or problem that needs to be solved.
- Acquiring and Cleaning Data: Collecting and preparing the data for analysis, including cleaning and transforming the data to ensure it is ready for analysis.
- Conducting Exploratory Analyses: Exploring the data to identify patterns, trends, and relationships.
- Drawing Conclusions: Using statistical techniques to draw insights and conclusions from the data, and communicating those findings to stakeholders.
- About two of the most popular computing languages for data science – Python and R: Python and R are two of the most popular programming languages used in data science. Python is known for its versatility and ease of use, making it a popular choice for a wide range of applications. R is specialized in statistical analysis and is favored by statisticians and data analysts.
- The different statistical data types that exist: There are four main types of statistical data:
- Nominal: Data that is classified into categories, such as gender, race, or nationality.
- Ordinal: Data that is ranked or ordered, such as academic grades or customer satisfaction ratings.
- Interval: Data that has a constant unit of measurement but no true zero point, such as temperature or time.
- Ratio: Data that has a constant unit of measurement and a true zero point, such as weight or height.
- How to gather insights from your dataset: To gather insights from a dataset, it is important to perform exploratory data analysis, use visualization techniques to identify patterns and trends, and apply statistical methods to test hypotheses and draw conclusions.
- How to evaluate what questions you want to answer and what types of questions are ideal for your scenario: To evaluate what questions to answer, it is important to consider the problem being addressed, the available data, and the business context. Questions should be specific, relevant, and actionable, and should be designed to address the specific problem being addressed. How to Describe what inference is, how it’s used, and be ready to tackle hypothesis testing and permutation testing: Inference is the process of making predictions or generalizations about a population based on a sample of data. It is used to test hypotheses, estimate parameters, and make predictions about future outcomes. Hypothesis testing and permutation testing are commonly used statistical techniques to test the significance of relationships between variables and draw conclusions about hypothesis testing and permutation testing: Hypothesis testing involves making statistical inference about a population parameter based on a sample of data. The process involves stating a null hypothesis, which represents the absence of an effect or relationship, and an alternative hypothesis, which represents the presence of an effect or relationship. A test statistic is calculated based on the sample data, and a p-value is calculated to determine the probability of observing the test statistic if the null hypothesis is true. If the p-value is smaller than the significance level, typically set at 0.05, the null hypothesis is rejected in favor of the alternative hypothesis. Permutation testing is a non-parametric method of hypothesis testing that does not rely on any assumptions about the distribution of the data. The process involves randomly permuting the labels of the data and recalculating the test statistic many times to obtain a null distribution. The p-value is calculated based on the proportion of permutations that result in a test statistic as extreme as or more extreme than the observed test statistic. Permutation testing is useful when the assumptions of parametric tests, such as normality or homogeneity of variance, are not met.
- How to describe classification and explain how it is used: Classification is a supervised machine learning technique that involves predicting the class or category of a new observation based on its characteristics or features. The goal of classification is to create a model that can accurately predict the class of new observations based on patterns and relationships in the training data. Common applications of classification include image and speech recognition, fraud detection, and customer segmentation. Classification algorithms include logistic regression, decision trees, random forests, and support vector machines.
After Completing This Course You Will Be Able To:
- Understand exactly what data science is and its real-world applications
- Identify each stage of the data science life cycle and understand the main goals of each stage
- Understanding data design and know-how sampling reduces bias
- Know the key differences between Python and R
- Familiarize yourself with and set up Jupyter on your computer
- Interact confidently with and read tabular data
- Understand what exploratory data analysis is
- Understand what data cleaning is and how it is used as well as the main questions to ask before cleaning
- Understand what data visualization is and how it’s used in data science as well as be prepared to visualize quantitative data
- Solve complex questions by bootstrapping your confidence interval.
- Know what the k-Nearest Neighbor algorithm is and how to use it to classify data
Here is what happy students say about this course:
“Great course! She explained every topic with examples that made it easier to understand.”
“Attention-grabbing. Clear explanation and amazing presentation.”
“I am a market research analyst. This course will help me in many ways to improve my Data analysis & Visualization skills. I have learned new things regarding the visualization of Qualitative and Quantitive data. There are plenty of things to learn and improve your skills. As a business and marketing professional, this course will help me in many ways. The instructor was great. I am satisfied with the price and the value it offered.”
“Great & informative course, I feel I want to continue to pursue Data Science”
Are you ready to become a Data Scientist? If yes click here to start learning.