Data Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data. It applies knowledge drawn from a broad range of application domains to real-world problems. Data science is related to mining, machine learning, and extensive data analysis. Data science uses techniques and theories drawn from many fields within mathematics, statistics, computer science, and information science to solve these problems. However, it differs from computer and information science by combining these fields with domain knowledge.
Foundations of Data Science
Data science is an interdisciplinary field of study focused on extracting knowledge from large data sets, solving problems in a wide range of application domains, and applying the knowledge and insights gained to inform high-level decisions. It incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, and visualization design, distributed and parallel systems, and machine learning.
Relationship to statistics
Data science is a relatively new field that is not synonymous with statistics. It focuses on problems and techniques unique to digital data, such as collecting and analyzing large amounts of information. Meanwhile, statistics emphasizes quantitative data and description, while data science deals with both quantitative and qualitative data (e.g., images) and emphasizes prediction and action. A distinction between the two fields has been made by Andrew Gelman of Columbia University, who has described statistics as a nonessential part of data science.
The Data Science Lifecycle
Now that you know what data science is, let's focus on the lifecycle. Data science's lifecycle consists of five distinct stages:
1. Capture: Data Acquisition, Data Entry, Signal Reception, and Data Extraction. This stage involves gathering raw data from various sources into a structured format for analysis and organization.
2. Maintain: data warehousing, data cleansing, data staging, and data processing. This stage covers taking raw data and transforming it into a form that can be used.
3. Process: Data mining, clustering/classification, data modeling, and summarization are among the most important steps in data science. A data scientist examines the patterns, ranges, and biases of prepared data to determine how useful it will be in predictive analysis.
4. Analyze: The exploratory/confirmatory, predictive analysis, regression, text mining, and qualitative analysis lifecycle involves performing analyses on the data.
5. Communicate: Business analysts communicate their findings through data reporting, data visualization and business intelligence. In the final step of the analysis, analysts prepare reports that are easy to understand and fit into a company's overall strategic planning needs.
IBM Data Science Professional Certificate
The IBM Data Science Professional Certificate Program provides a solid foundation in data science, with an emphasis on the role of the data scientist in our world and the approaches they use to solve real-world challenges. You will gain familiarity with popular tools for data science, including Jupyter notebooks, RStudio IDE, and IBM Cloud; as well as experience in using data science methodology to build, test, and train models.
In addition to learning Python, you will work with pandas, Numpy, and BeautifulSoup. You will create a project in Python to test your knowledge of these libraries. Other skills you learn during the course will enable you to design data science models and artificial intelligence applications.
Through practice on real-world data sets, you will develop a thorough understanding of the role of SQL and databases. You will also create impactful visual representations with Python data visualization libraries—Matplotlib, Seaborn, Folium, Plotly, and Dash—and learn machine learning including regression, classification, and clustering.
You will design and build a data model that addresses a real-world issue. Your data model will be submitted to IBM for evaluation.