You’re not the only one thinking of taking a course in data science. Data science, which is growing rapidly, is a mixture of statistics and machine-learning. This growing field is complex and there are many courses that can teach you all you need. These are the three courses that you should take to get started on your career. The first course focuses on exploratory data analytics, while the second focuses more on machine learning.
Data science is a sub-field of computer science.
Data science emerged from computer science. Peter Naur, one of the early pioneers in data science, described the fundamental aspects of data science in 1974 in a book. At a 1996 conference, the International Federation of Classification Societies (IFCS), first used the term “data science”. William S. Cleveland, a 2001 author in the International Statistical Review introduced data science as a distinct discipline. He suggested that statistics could be extended beyond the traditional areas of technical analysis and application. Data science quickly developed from these humble beginnings into a rapidly growing research tool.
Data scientists are responsible for analyzing large amounts of data and creating predictive models. Data science uses machine learning, artificial intelligence, and other statistical methods to analyze data and make informed decision. Data scientists are responsible to develop and apply mathematical and statistical models that solve real-world problems. Data scientists are also responsible to create the foundation for data-driven decision-making. Data science is a rewarding and fast-growing field.
There are some similarities between computer science and data science. Both are essential for modern computing. The first deals with modern computing theory and practice and includes coding and base hardware. Data science, however, is concerned with data generated by different sectors around the globe. Computer scientists are experts in computing; data scientists concentrate on data science and how it can structured and analyzed. This field is crucial in today’s technology world. This field can help to save the environment and lead to amazing inventions.
Data science is an interdisciplinarity that draws on both mathematical and statistical approaches. It is necessary to combine large amounts of data and produce actionable, predictive, or descriptive models. Big data is a complex area that requires creative insights from large amounts of information. Big data is often too large to store in one computer. These skills make data science a great choice.
Computer science is a broad field that covers theoretical research into the functions of computers, networking protocols, data, and other related topics. Data science, however, is the application of mathematical, statistical, and other skills to different types of data. As businesses and individuals use data to make better business decisions, this field is rapidly growing. Computer science is an expanding field that has many facets. What makes data science unique?
Data science and computer science are becoming increasingly intertwined. Data scientists develop applications that allow data analysis to be possible. Data scientists use computer science-based algorithms that predict the outcome of data collection, and then analyze trends and patterns. Coding is an integral part of a high-quality data science program. For a successful career, you need to be a highly qualified data scientist and computer engineer. Some of the best engineering colleges offer international certification programs and opportunities for value-added learning.
It is a part of statistics
Statistics is a branch in mathematics that offers programmatic tools and methods to analyze and interpret data. These applications include data collection and analysis, experiment design, and the determination of values for particular questions. These methods are used by statisticians in almost every industry: finance, medicine and government. Although some may argue that data science and statistics are distinct, they have many similarities that can be combined to make better decisions.
Statistics is booming. According to the Bureau of Labor Statistics, there will be 15,000 new jobs within the field between now 2029. The BLS predicts that the field will expand by 35 percent over the next decade. This is much faster than the average. There are many ways to get involved in this field, with so many applications.
The Cornell University Department of Statistics and Data Science is a research center that conducts research in a wide range of fields. This department conducts research in a variety of fields including pure mathematics and cutting-edge areas like genomics, finance, public policy, and others. This department trains students in machine learning and statistics. Their research projects often advance fundamental advances in areas such as genetics and neuroscience. Every day, the field of statistics and data sciences is expanding in both scope and applications.
Data science, when combined with programming, allows us to analyze large amounts of data and use the results to solve real-world problems. These results are then fed back into the operational systems. Using data from the Wide-field Infrared Survey Explorer, the comet NEOWISE was found. Data mining is a term used in the field of information technology. Both fields have many tools and techniques that can be used to analyze large quantities of data.
Strong mathematical skills are required for the field of statistics. Statisticians are able to analyze large quantities of data and present it in an understandable format to others. Data science requires business acumen, critical thinking, and excellent interpersonal communication skills. Students in this field must have knowledge of math and statistics. Programming languages such as computer programming are also useful. Data science requires a wide range of skills.
Data science is a methodological discipline that focuses on the development of tools and methods to conduct empirical investigations. Data science’s primary goal is to identify the strengths and weaknesses in different approaches to learning about reality. Data scientists use data to make better decision. Statistics has many applications. As data science grows in popularity, so does the range of its applications. Its applications are virtually limitless.
It is a part of machine learning
Data science is an interdisciplinarity field that uses a variety of scientific methods, algorithms, systems, and techniques to make sense out of large amounts of data. This field aims to extract the appropriate information from large amounts of data, and guide technology and science decision-making. Machine learning can be used to detect trends and patterns in data. Data scientists must be proficient in statistics, programming languages, big data tools, and other relevant topics.
Artificial intelligence is based on machine learning. This branch of computer science can be used to automate tasks that would otherwise require a lot of human effort and make decisions without any human intervention. Machine learning algorithms have made it possible to detect fraud, prevent large monetary losses, perform sentiment analyses, and many other things. Data science can improve the lives of individuals, businesses, governments, and nations around the world. Data science allows companies to analyze and predict future trends using their business data.
Data science can be used by companies to analyze data and improve their products and services. Machine learning can be used to create recommendation systems that recognize friends and identify the location of images. Data science is used to provide recommendations in many games today. Data science games can be updated as players progress through the levels. Data science applications include PriceRunner, Junglee and Shopzilla. They pull data from relevant websites in order to make informed decisions about the next purchase.
Machine learning algorithms are used to teach robots and computers how to explore the world. A machine learning algorithm is neural networks, for instance. These algorithms use huge amounts of data to identify patterns and rules. There are many types of neural networks. Each one is better for a specific task. Data science is the study of how to train these algorithms in order to produce accurate models for specific datasets. This is an interdisciplinarity field that has many applications.
Data science is already being used in many industries. Data science’s predictive capabilities have been shown to optimize strategic planning and improve production processes. Both large corporations and startups today collect data to increase their revenue. The more data they gather, the more insight they can draw. Data scientists can use predictive analytics, such as lead scoring to inform business decisions. What is data science?
Machine learning algorithms have improved their ability to produce useful results. They still require humans to refine them and constrain them. Machine learning algorithms are not able to do all the work in the banking industry. A program may still require a programmer or engineer for refinement. Although machine learning algorithms are sometimes more complicated than traditional solutions, they are also often used in many industries.
Data Science’s importance in today’s technologically driven world cannot be overemphasized as the world relies on information and stores data for most of its day to day activities. You don’t need to be told that information is the new currency of the world.
This course gives insight into what data science is and experiences sample simulations and case studies that would immensely help students learn the RESTful API calls to the Foursquare API and retrieve data information about venues in different neighborhoods worldwide. Applied Data Science Capstone is a unique course offered by IBM under the Coursera catalog.
You will learn how to use the Folium library to map geospatial data and easily communicate your results.
You will earn a Certificate after the course completion, which comes with a digital badge from IBM.
One good advantage of the course is that it’s subtitled in many major world languages like French, Portuguese, Chinese, Italian, Spanish, Russian, and even Arabic. You can also learn at your speed, which means better understanding.
The course comes in four segments (4)
- Foursquare API
- Neighborhood Segmentation and Clustering
- The Battle of Neighborhoods
- The Battle of Neighborhoods (Concluding part)
Genomic Data Science is part of the Genomic Data Science Specialization offered by Johns Hopkins University. As a data science prerequisite, you get in-depth knowledge and skillset in Python Programming, Bioinformatics, Biopyton, and Genomics. With over 100,000 students enrolled in this course offered on Coursera, the course offers a full package
You will learn newer resources that will help you better analyze and understand next-generation sequencing experiments like Python, Galaxy, and Bioconductor. This course is ideally suited for molecular biologists or scientists needing experience with data science computational methods.
During the course session, you will be able to try your hands on some projects for you to qualify and earn a sharable certificate
The course outline contained in this section include;
- Introduction to Genomic Technologies
- Genomic Data Science with Galaxy
- Python for Genomic Data Science
- Algorithms for DNA Sequencing
- Command Line Tools for Genomic Data Science
- Bioconductor for Genomic Data Science
- Statistics for Genomic Data Science
If you are part of corporate and middle management, this course will be ideal for you as it will allow you to promote data-driven creativity. Topics address important topics and perspectives into data uses. It also includes data mining, machine learning approaches, pros and cons, and functional applicability problems.
The course introduces you to data science, why it is essential in various sectors, the value data science can generate, what big data can solve, the distinction between descriptive, predictive modeling data analysis, and the functions of cognitive computing. This course covers, from an analytical point of view, supervised, unsupervised and semi-supervised methods that can be learned from processes of sorting, clustering, and regression; NoSQL data models and innovations; and the function and impact of map-reducing and analog paradigms-based scalable cloud-based computing systems.
During the course session, you will try your hands on some projects to qualify and earn a sharable certificate.
Below are the modules on this specialized course;
- Introduction to Data-driven Business
- Terminology and Foundational Concepts
- Data Science Methods for Business
- Challenges and Conclusions
This specialization curriculum is self-paced and structured to help you learn unique job skills within a short time. Offered by the UCDAVIS, this specialization needs little or no experience in programming, as you will be taught from scratch on data and SQL queries.
You will cover vital topics like SQL fundamentals, SQL, Analysis, AB testing, and distributed computing using Apache Spark.
As you proceed further in this section, you will learn how to write queries, filter, sort, summarize, and even manipulate data. Using the data brick workspace, you will be able to create an end to end pipeline that can read and transform data.
Students who choose this course will be able to secure a job in any sector as a Database Administrator or Program Analyst
These four (4) modules in this specialized course are;
- SQL for Data Science
- Data Wrangling, Analysis and AB Testing with SQL
- Distributed Computing with Spark SQL
- SQL for Data Science Capstone Project
Data science is one of IBM’s numerous data science specialization due to their long-standing in the aspect.
As a student, taking this course will expose you to the application of data in real life. The life experience projected through this online course is a real deal as you will gain valuable insight into both data science and machine language: application and use cases. At the end of the course, your mindset would have changed, and you will be thinking more like a data scientist as you will be able to apply what you have learned to real data science problems.
Some of the skills and software you will be taught how to use are Watson Studio, JupyterLab, GitHub, and R Studio.
This course covers the following sections;
- What is Data Science?
- Tools for Data Science
- Data Science Methodology
- Python for Data Science and AI
- Databases and SQL for Data Science with Python
- Data Analysis with Python
- Data Visualization with Python
- Machine Learning with Python
- Applied Data Science Capstone
You don’t have to be told that information is the new currency of the world. Such is this course offered by John Hopkins University. Data Visualization & Dashboarding with R is a five in one module package that builds on your prerequisite foundation in data. Industry experts will teach you how to visualize data using R works. You will create static and dynamic data visualizations that you get to publish on the web.
Getting to the end of the course outline, you will become an expert in data visualization with a verified certificate.
Below are the modules you will find in this specialization;
- Getting Started with Data Visualization in R
- Data Visualization in R with ggplot2
- Advanced-Data Visualization with R
- Publishing Visualizations in R with Shiny and flex dashboard
- Data Visualization Capstone
Are you interested in GIS principles and strategies and want to practice on your own? Then this course is for you. This is a specialization that is perfect for newbies in mapping and GIS. A course taught by the University of Toronto via the distance learning platform of Coursera is an excellent opportunity to learn one of the most sorts after skills in the market. You get to learn interpreting map data skills using several data types and approaches to addressing spatial questions. You will also be introduced to data set processing using various forms of queries to locate the data you need to answer a specific query. As you build on the course specialization, you will go through techniques and training to analyze and use vector data to find spatial correlations within and between data sets.
Modules you will find in this specialization are;
- Filtering Data Using Queries
- Vector Analysis
- Remote sensing as a GIS data source
- Raster Analysis
- Project: Spatial Analysis
As a top leading University, Michigan offers students worldwide opportunities to learn Applied data science through the Coursera platform.
The course will give you insights into data science, the application of data, techniques, and data analytics.
Learning this course will broaden your knowledge and gain much-needed skills like Python Programming, Data Visualization, Machine Learning Algorithms, Data Cleansing, Scikit-Learn, Text Mining, and many more.
Applied Data Science with Python is a five-course (5) curriculum for students with sound knowledge (intermediate) of Python Programming and serious about learning how to apply data visualization to real-life scenarios.
Find modules included in this course;
- Introduction to Data Science in Python
- Applied Plotting, Charting & Data Representation in Python
- Applied Machine Learning in Python
- Applied Text Mining in Python
- Applied Social Network Analysis in Python
Excel will always be a part of enterprise business as it’s widely popular software in the workspace. This excel course offered by MACQUARIE University is a great one, given that it’s a valuable fundamental asset for securing an IT job.
Choosing this course on Coursera is ideal for a new entrance into IT. You will be exposed to the foundation of Excel as its business applications, expand your knowledge skills in managing datasets, and create meaningful reports.
At the end of the course, you will be job hunting ready as you will have gained the required skillset in Microsoft Excel, concatenation, Pivot Chart, and table.
You get to obtain a shareable certification after the course.
Below are topics you will cover during the course;
- Working with Multiple Worksheets & Workbooks
- Text and Date Functions
- Named Ranges
- Summarising Data
- Pivot Tables, Charts, and Slicers
- Final Assessment
Analyzing Big Data with SQL is the latest in-demand database course in the Coursera catalog, and students need to learn the skill to stay relevant in today’s IT Industry. Offered by CLOUDERA, analyzing big data with SQL will give you an in-depth understanding of SQL functions. This course focuses more on the big data SQL engines APACHE Hive and APACHE Impala, which means you will learn how to explore and query databases using various tools. You also lean to a group and aggregate for easy anwer to analytical questions.
As a prerequisite, a virtual machine needs to be installed on your computer to learn this course.
This course is ideal for learners interested in venturing into database management and administration as you will learn the basics of SELECT statements, filter results, answer analytical questions, as also work with sorting and limiting output
Skills you will learn but are not limited to are Apache Impala, Big Data, SQL, Apache Hive, Apache Analysis, and many more.
You will earn a Certificate upon completion that can be shared with employers of labor.
Modules below are exciting topics you will cover in this course;
- Orientation to SQL on Big Data
- SQL SELECT Essentials
- Filtering Data
- Filtering Data
- Grouping and Aggregating Data
- Sorting and Limiting Data
- Combining Data