Start Date: 06/27/2021
Course Type: Specialization Course |
Course Link: https://www.coursera.org/specializations/data-analysis
Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world's biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions.
Data Management and Visualization
Data Analysis Tools
Regression Modeling in Practice
Machine Learning for Data Analysis
Learn Data Science Fundamentals. Drive real world impact with a four-course introduction to data science. Data Analysis and Interpretation Specialization The Data Analysis and Interpretation Specialization is a hands-on, program-based experience for those who want to analyze and interpret data for their own research, or to use in professional or academic contexts. Designed by Clemson University researchers in the Data Analysis and Interpretation Specialization, this course gives you a great opportunity to explore what data analysis is and what it means to access, interpret, and analyze data. You will learn about basic techniques for interpreting data, and you will be challenged to apply what you’ve learned to interpret data for your own projects. When you’ve got a problem to solve, you’ll first construct a data analysis problem statement, and then you’ll use the basic techniques to interpret the problem statement in a way that brings it into focus. This course focuses on the interpretation of data and the analysis of data through a problem solving approach, and it also encourages reflection and the exploration of problem solving methodology in the future. The specialization is designed to help you gain practical skills in interpreting and manipulating data, and to develop the ability to interpret and manipulate data for problem solving. You’ll learn to interpret data using tools to get useful information to ask useful questions about data and about your own projects. We’ll use data to develop problem solving abilities, and we’ll use problem solving skills to get ahead in problem solving.Problem Solving and Problem Interpretation Data Analysis Concepts Data Manip
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Data analysis | Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term "data analysis" is sometimes used as a synonym for data modeling. |
Data analysis | The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions: |
Geometric data analysis | Geometric data analysis can refer to geometric aspects of image analysis, pattern analysis and shape analysis or the approach of multivariate statistics that treats arbitrary data sets as "clouds of points" in "n"-dimensional space. This includes topological data analysis, cluster analysis, inductive data analysis, correspondence analysis, multiple correspondence analysis, principal components analysis and . |
Data analysis | Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis. |
Data analysis | Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis. |
Data analysis | In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested. |
Data analysis | Data initially obtained must be processed or organised for analysis. For instance, these may involve placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software. |
Computer-assisted qualitative data analysis software | Computer Assisted/Aided Qualitative Data AnalysiS (CAQDAS) offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation, recursive abstraction, content analysis, discourse analysis, grounded theory methodology, etc. |
Data analysis | Analysis of data, also known as data analytics, is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. |
Data analysis | Statistician John Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." |
Data analysis | The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. |
Data analysis | Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories. |
Data analysis | The data necessary as inputs to the analysis are specified based upon the requirements of those directing the analysis or customers who will use the finished product of the analysis. The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). |
Multiway data analysis | Multiway data analysis is a method of analyzing large data sets by representing the data as a multidimensional array. The proper choice of array dimensions and analysis techniques can reveal patterns in the underlying data undetected by other methods. |
Data analysis | During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken. |
Forensic data analysis | The analysis of large volumes of data is typically performed in a separate database system run by the analysis team. Live systems are usually not dimensioned to run extensive individual analysis without affecting the regular users. On the other hand, it is methodically preferable to analyze data copies on separate systems and protect the analysis teams against the accusation of altering original data. |
Social data analysis | Social data analysis is a style of analysis in which people work in a social, collaborative context to make sense of data. The term was introduced by Martin Wattenberg in 2005 and recently also addressed as big social data analysis in relation to big data computing. |
Data analysis | Nonlinear analysis will be necessary when the data is recorded from a nonlinear system. Nonlinear systems can exhibit complex dynamic effects including bifurcations, chaos, harmonics and subharmonics that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to nonlinear system identification. |
Data analysis | After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase. |
Social data analysis | Social data analysis comprises two main constituent parts: 1) data generated from social networking sites (or through social applications), and 2) sophisticated analysis of that data, in many cases requiring real-time (or near real-time) data analytics, measurements which understand and appropriately weigh factors such as influence, reach, and relevancy, an understanding of the context of the data being analyzed, and the inclusion of time horizon considerations. In short, social data analytics involves the analysis of social media in order to understand and surface insights which is embedded within the data. |