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What are data analytics courses?
Data analytics is the method of using mathematical analysis and logical methods to derive knowledge from data. When carried out meticulously and effectively, data analysis findings will be an invaluable supplement to qualitative study in providing information for decision-making. Data analytics may sound like data science, but there are major variations. Data scientists usually come from computer science and programming backgrounds and focus on coding expertise to construct algorithms and analytical models. Usually, data analysts have backgrounds in statistics and analytics and often use these computational methods to respond to real market issues.
Data analytics is a very old idea that became more important as analysts started using machines to help make business choices in the 1960s. Today, organizations are impacted by data, regardless of scale, location, and industry. Several companies utilize data in different business functions, including inventory management, customer support, and development. Data analysts may help business executives make smarter choices by identifying patterns and solving market challenges, such as why revenues fell in a given quarter. Although data analysts and data scientists both deal with data, what they do with the knowledge varies. By seeking responses to a series of questions using statistics, a data analyst assists market executives in decision-making. On the other hand, a data scientist creates his queries, designs tests, and develops new algorithms.
Online workshops are a perfect place to acquire data processing expertise. Multiple online sites provide technical degrees, MasterTrack certificates, specializations, and data analysis courses from top colleges such as Michigan University and organizations such as the Academy of International Business Management. Many classes, such as introductions to data science and data science programming with SQL, Python, and R, are common to data science and data analysis. Courses more basic to data analytics are also accessible, such as data analysis and comprehension skills, company Excel skills, exploratory data analysis, and several statistics and probability courses. As the need for computer expertise has grown, data analytics qualifications and courses have increased in popularity. The best data analytics courses allow students to learn key programming languages, such as, among others, Python, R, and SQL, and gain real experience with:
- Collecting data
- Analyzing Results
- Visualization of Results
- Analysis of data
- Prognostic analytics
Data analytics qualification is a valuable investment since it can help you improve the data expertise that companies progressively focus on, offer you more chances to grow with your new employer, and contribute to higher pay. Data analytics courses were designed to obtain hands-on experience uncovering new perspectives from data sets, creating data-driven forecasts, and creating compelling data visualization. To help professionals take advantage of these resources. Data analysts do not do as much coding as data scientists, but learning your way through those programming languages is still important. In particular, the industry norm for accessing massive datasets is SQL (Structured Query Language), and statistical programming languages such as R or Python are important for carrying out advanced analysis of this information.
Data analysts often focus on more traditional market systems. Although Microsoft Excel is not as efficient as SQL, R, and Python, it will get the job done when dealing with comparatively smaller databases. It could be the best (and cheapest) early-stage lean startup method for the job. Data visualization and presentation capabilities are often an essential aspect of the work, which generally entails learning new applications such as Tableau and improving traditional business tools such as Excel and Powerpoint.
What are the best courses for data analytics?
Data analytics is a branch of data science that deals with data processing and interpretation and uses different methods to turn it into useful knowledge that can be used for decision-making and enhancing organizational efficiency. Data analytics aims to produce data insights by linking operational priorities with patterns and trends. A typical case in data analytics is comparing data properties with corporate assumptions, and the methodology aims to concentrate on market and policy. The following online data analytics courses will help you know about becoming a successful data analyst.
Data Analyst Nanodegree Program on Udacity
For students familiar with Python (and in particular NumPy and Pandas) and SQL programming, Udacity’s Data Analyst Nanodegree Program is ideally adapted. This nano-degree curriculum is divided into four courses: Data Analysis Presentation, Functional Statistics, Data Wrangling, and Python Data Visualization. Videos and quick quizzes are used to teach course content. With a chat GUI installed inside the classroom, you can communicate with other students. A project or two is also included in any lesson. For instance, students have to explore weather patterns in Introduction to Data Analysis and investigate one of Udacity’s curated databases using Numpy and Pandas.
The great thing about this initiative is that on all their assignments, students get individual reviews. Furthermore, all learners undergo job counseling. It takes students, on average, four months to complete the curriculum from beginning to finish if they spend at least 10 hours a week studying. Udacity’s Data Analyst Nanodegree Program will teach you how to say a story with data, as long as you have some experience with Python and SQL. The software incorporates some of the most common data analytics resources, such as R, Python, and Tableau. It helps you apply principles that you have only studied to real-world ventures inspired or provided by businesses in the field.
Data Science Specialization on Coursera
Coursera’s Data Science Specialization is a 10-course curriculum that allows you to grasp data science’s whole process at a fundamental level. While anybody can sign up for this course, students should have Python novice level experience and some knowledge of regression. The program is taught through videos and additional readings. The curriculum culminates with a hands-on assignment that offers students an incentive to generate a functional data product.
Most students should expect to complete the program in about 11 months, according to Coursera, as long as they spend 7 hours a week training. Since Coursera charges a monthly fee, the specialization is completed as soon as possible with good motivation. Students may still opt to access the course material for free, but this means they will miss certification and the capstone project. There is a 7-day free trial if you are not sure if the course is right for you. Coursera also helps applicants who cannot pay the course fee with financial assistance.
Data Analytics Immersion on Thinkful
Data Analytics Immersion from Thinkful is an intensive program for full-time training. Although it is one of the most expensive courses out there in data analytics ( $12,250), it promises to take you from beginner to expert in just four months. Students, however, are expected to spend between 50 to 60 hours studying a week. You receive a customized timetable to help you stay on track once you sign up for the course.
Seven fields are included in the curriculum: Excel Foundations, Storytelling with Info, SQL Foundation, Tableau, Market Analysis, Python Foundations, and Capstone. Students get to create a final project during the Capstone Process. The content for the course is text-based. As such, the software will not accommodate people who want training via videos. Each student gets a personal tutor who can answer questions about the course. You also have a career consultant and a performance manager from academia. During difficult moments, the latter will help you fall behind and need to push your deadlines back.
Business Analytics Specialization on Coursera
This five-course series seeks to teach students how to use big data in economics, human resources, communications, and operations to make data-driven business decisions. The Business Analytics Specialty is categorized into four discipline-specific courses developed by the Wharton School of the University of Pennsylvania and hosted by Coursera.
Videos and texts are used to teach students, and knowledge is assessed via mandatory quizzes. Students complete a Capstone Project designed in association with Yahoo at the end of the course. The completion of the entire specialization takes about 40 hours, which means that if they spend three hours a week learning, students can complete the program in just six months.
How do I become a data analyst?
Among the top employment avenues in the 21st century are the data analytics and data science fields. The possibilities of data analytics are enormous in the emerging data-savvy environment. You will seize attractive job opportunities with the right experience and skills. If a person wishes to begin a career in data analytics, some requirements are involved, and it needs both technical qualifications and skills. A prospective data analyst is expected to graduate from a data analysis program and have a high GPA. And suppose you do not have a specialty in data analysis, a degree from a well-reputed university in mathematics, statistics, or economics will quickly land an entry-level position in data analysis.
Many entry-level data analyst positions need at least a bachelor’s degree. Typically, upper-level data analyst positions guarantee a higher salary and enable you to get a master’s or doctoral degree. It is advantageous to get a master’s degree in Data Analysis or Business Analytics. You should pursue a master’s degree if you are interested in data analytics. Other than the degree, which affects you the most, you can even enroll in online classes. Therefore, the direction you follow to be an eligible data analyst can be anything.
You should be fluent in at least one programming language as a data analyst. The more languages you’re fluent in, though, the better it is. R, Python, C++, Java, MATLAB, PHP, and more are common programming languages used to manipulate data. You should be familiar with languages as a data scientist, such as R, HIVE, SQL, and more. An integral part of data processing is constructing queries to retrieve the desired data. If the data is processed, you may need to produce reliable reports. SAS, Oracle Visual Analyzer, Microsoft Power BI, Cognos, Tableau, and more are a few standard tools.
The role of a data analyst is to provide decision-makers with comprehensive and reliable details. Data analysts must also consider the customer’s basic requirements and have a deep knowledge of the data. For cooperation with the different stakeholders, administrators, IT experts, excellent communication skills are important in ensuring that the data aligns well with the company goals. The research conducted by a data analyst eventually modifies/improves certain business processes.
Statistical expertise and the proper level of familiarity with the formulas are needed to have real-world utility for interpreting results. It would help if you had a good understanding of mathematics as a data analyst. You should address basic market issues, such as measuring compound interest, inflation, mathematical measures, etc. You should also know how to use tables, maps, diagrams. It is important to be comfortable with college-level algebra, thus making knowledge visualization more desirable. For data analysts, understanding linear algebra and multivariate calculus is very useful because they are commonly used in data processing performance.
Among the key activities of data, analysts are arranging data and estimating numbers. Hence, if you are comfortable with Excel, it is helpful. You will learn how to use Excel to its full capacity from many excellent online outlets. To become a data analyst, you must first obtain a Bachelor’s degree, a prerequisite for most entry-level data analyst jobs. Finance, economics, mathematics, statistics, computer science, and information technology are related fields that also provide useful knowledge.
Data analysis is about understanding emerging patterns and technology, making it important for a data analyst to contribute to lifelong learning for himself. If you plan to become a data analyst but have no industry background, you will start with a degree in an online data analyst course. The course will make your base solid, help you create practical projects, and learn and improve your ability. To gain experience and contribute to your resume, you can go on to an internship or take up some consulting jobs to stand out and get an edge when applying for a high-profile position as a data analyst.
Big data analytics
Big data analytics analyses vast volumes of data to discover secret trends, associations, and other observations. It is easy to analyze the data and get answers from it almost automatically with today’s technology. The big data idea has been around for years; most companies today realize that they can apply analytics and get tremendous benefit from it if they collect all the data that streams through their enterprises. But back in the 1950s, decades before anybody uttered the word “big data,” organizations used simple statistics to discover insights and patterns, numbers in a manually checked spreadsheet.
However, the latest advantages that big data analytics brings to the table are speed and efficacy. Whereas an organization may have accumulated data, run analytics, and unearthed knowledge a few years ago that could be used for future decisions, the company will find directions for immediate decisions today. The opportunity to operate faster and remain flexible offers companies a strategic advantage that they did not previously have. Big data analytics lets businesses leverage their knowledge and use it to discover potential possibilities. In essence, this leads to smarter strategic moves, more effective practices, better income, and happy clients.
When it comes to managing vast volumes of data, big data technology such as Hadoop and cloud-based analytics offer major cost advantages and help find more effective ways of doing business. With Hadoop and in-memory analytics’s speed and the ability to analyze new data sources, organizations can quickly analyze information and make decisions based on their findings. The power to give clients what they want comes with the opportunity to assess consumer desires and loyalty through analytics.
Big data analytics is the often dynamic method of analyzing big data to discover knowledge that can help companies make better business choices, such as secret patterns, correlations, industry dynamics, and consumer desires. Big data is a mixture of organized, semi-structured, and unstructured information gathered by companies that can be used in machine learning programs, predictive processing, and other sophisticated analytics applications to retrieve information. Although big data does not correlate to any single data volume, big data implementations also contain terabytes (TB), petabytes (PB), and even exabytes (EB) of time-captured data.
Big data analytics is a type of applied analytics that includes complex applications with predictive models, mathematical algorithms, and what-if analysis. Big data analytics can lead to positive business-related results by specialized systems and software:
- Fresh prospects for sales
- Marketing can be more effective
- Better Support for Consumers
- Enhanced quality of operations
- Competitive perks over adversaries
Big data analytics applications allow the study of increasing amounts of organized transaction data by data analysts, data scientists, statisticians, and other analytics practitioners, plus other types of data that are often left untapped by traditional analytical programs. It entails a combination of data that is semi-structured and unstructured. For example, internet clickstream data, web server logs, content from social media, consumer email text and survey responses, cell phone records, and computer data gathered by internet-related sensors of items are some of these data.
Qlikview
QlikView is a product for data exploration to create tailor-made analytics applications and dashboards for market challenges. The software helps users discover data observations and associations using QlikView’s Associative Data Indexing Engine through diverse outlets. For sharing knowledge, QlikView provides driven experimentation, discovery, and shared analytics. Also, without technical development skills, the software helps consumers create and deliver analytical applications that help to push quicker responses to evolving market needs and greater visibility within an enterprise.
Qlikview is also a useful tool for business intelligence that is used to transform raw data into information. This software works like a human brain that can look for answers in various directions. QlikView also includes functionality for ad hoc requests, which enables fast decision-making based on readily accessible details. The software is the traditional analytics tool from Qlik to accelerate fully immersive driven analytics software and dashboards, offering information to address market problems. With the introduction of QlikView and the game-changing Associative Engine it is based upon, the digital analytics era has begun.
QlikView is very effective in visually analyzing the interactions between information. It performs in-memory data processing and stores the information it produces. Data from many sources, including files and relational databases, can be read. By doing advanced analytics on the data they have, it is used by companies to get deeper insight. By combining data from different sources into one QlikView analysis document, it even does data integration. QlikView is the first-generation BI solution that propelled the market transition from massive, IT-driven, stack-based solutions to more agile approaches to data discovery, kicking-off the revolution in data analytics. To solve business challenges, customers readily embraced QlikView because it allowed them to easily build fully immersive dashboards and analytics applications without relying on stack technologies. For two decades, QlikView has created considerable value across all major industries and geographies for customers of all shapes and sizes.
Advantages/ benefits of using QlikView include:
- Using the platform is easy and very intuitive.
- It provides visualization of data in a meaningful and creative way
- There is immediate response time with no limitation on the amount of information
- It enables you to, directly and indirectly, search through all data
- There is support for dynamic applications, dashboards, analysis, etc.
- It facilitates quick integration into a single application of data from different sources.
- It helps you identify trends and data to make the most innovative choices.
- It allows you to access, analyze, and retrieve mobile device data.
- It provides a rapid return on investment due to a short period of implementation.
Three key components are involved in the Qlik view architecture. QlikView Desktop is a programming platform that developers use to build the QlikView data model and GUI Interface for frontend use. QlikView Server hosts applications created in the desktop version so that users can access and use them anywhere. A QlikView server also manages contact between clients/end-users. The QlikView Publisher is an optional mobile component for the server-side portion of QlikView. This component helps you fetch data directly into QlikView files from a data source, such as normal data files, salesforce, and load data.
Data analytics courses in South Africa
Many South African universities have forums for interested citizens in South Africa to learn critical data analytics skills. These courses are mainly classroom teachings from universities in South Africa. Some of these institutions are listed below.
Data science program from Explore data science academy
The EXPLORE Data Science Academy is South Africa’s premier Data Science Academy, which teaches students the skills that matter: how to address real-world challenges using the newest Data Science developments. The Approved Skills Program for Data Science is an intensive one year course that teaches students what they need to know:
- How to function like a Pro with SQL.
- How to code in Python.
- How to use new algorithms for machine learning.
It is based on campuses in Cape Town and Gauteng and runs for one year. The course is fully funded and includes students’ living expenses. For the course, you do not require any unique credentials. You only need to demonstrate a good capacity to solve complicated problems, a grasp of statistics, and a determination to make a difference. A set of demanding aptitude tests are the foundation of the screening criteria.
GetSmarter’s data analysis short course from the University of Cape Town
You will practice real-world data sets in this online data analysis course and learn useful skills to drive better decision-making in your company. With a sought-after skill set and a degree of data processing, backed by UCT, you will finish this course and become an asset to every organization. It is available online and lasts for an eight-week duration. It requires about 9 hours a week of effort to take this course, and it costs R14500. No qualifications are necessary to take this course.
Data science analytics from Knowledge Academy
This one-day course will help you improve your skills in becoming a professional data analyst. You would successfully research multiple forms of data and turn it into a reliable source of knowledge by taking this course. You may also study different theories, including digital, technological, and theoretical techniques. It is situated in Johannesburg, Pretoria, Port Elizabeth, and Cape Town. It costs around R28,365, and the course has no prerequisites.
Introduction to Data Analysis using Stata
The course is for anyone new to Stata and for anyone who wants a basic refresher from Stata. As well as being able to use Stata effectively for basic visualization and graphics, you can learn strategies for the everyday use of Stata. Some of the topics discussed include: the framework and atmosphere of the Stata menu, opening datasets and collecting descriptive statistics, maintaining the session performance, limiting commands to data subsets, data regression, running batch commands, basic graphs. This course is taken at the University of Cape Town, and R 4 200.00 is the course’s cost. Partial scholarships for bonafide students are available.
University of the Western Cape’sCape’s Postgraduate Diploma in Data Analytics and Business Intelligence
Business administration, information technology, data science, and statistics are included in this interdisciplinary theoretical and qualitative studies program and accompanied by an internship during which applicants need to show their abilities in solving difficult problems. The college is in Cape Town, and the length of the course is 18 months. The course is a part-time program, costing R50 000. A B-degree with an average of fifty-five percent for modules in information technology, business administration, data science, or analytics – or a B-degree (or equivalent) with two years of appropriate working experience – are entry requirements for the degree.