Top 7 Online Data Analytics Courses With Placements
Data analytics is fetching ground like never before. Data analytics has brought a quantum change in the way we live and businesses do their business. Corporations, start-ups, SMEs, and large organizations around the world are generating huge amounts of data to reduce costs, focus on target marketing, and optimize existing processes. One thing is certain, data analytics will be the core of countless technological solutions. In this article, you will find the best online data analytics courses to help you learn the process.
People already use analytics for making various decisions in their lives. From choosing the best routes to reach a destination to the identification of waste using quality tools.
Data analytics is crucial to many organizations and there has never been a better time to add that skill to your resume. With more people using online tools to analyze data, companies are finding it easier to understand the data they’re collecting.
What Can Data Analytics Do?
Data analytics helps in improving decisions and having a better understanding of a phenomenon. If I say by learning online data analysis courses, you could answer questions such as – should I buy more stocks, is a particular type of medicine effective, or how much any product can fetch sales in any season?
Then, it wouldn’t be wrong because if you can accurately predict how much product can grab your paying customer you can plan your procurements to meet the demand. If you can prove the effectiveness then you could be helping people’s health and well-being. Conversely, if the results don’t come as desired then you could also prevent people from buying the medicine.
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Data Analytics – Formats and Types
1) Big Data
2) Structured and Unstructured Data
3) Metadata
4) Real-time data
5) Machine data
Big Data –
Big Data is a term to describe a huge volume of complex data that databases of traditional database management applications can’t use. Big Data is characterized by the 5 V’s which are Volume, Velocity, Value, Variety, and Veracity. Volume determines the quantity of data, Velocity refers to the speed of data growth, variety describes its source and Veracity refers to the quality of data.
Structured and Unstructured Data –
Structured Data is a traditional data model of rows and columns whereas unstructured data is in the form of pictures, text, or video.
Metadata –
Metadata is another form of data that reads and provides information about other different data such as unstructured data.
Real-time Data
Data that is processed and presented at the same time is called Real-Time Data. Real-time data is very useful in stock markets to make decisions upon information received at that moment.
Machine Data –
Data generated automatically from machines such as physical devices, software, and clouds collectively called IoT ( Internet of Things) is called Machine data.
Types of Data Analysis
- Diagnostic Analysis
- Predictive Analysis
- Prescriptive Analysis
- Statistical Analysis
1. Diagnostic Analysis
Diagnostic Analysis identifies the root cause by identifying and collecting additional data and reporting to outliers about why the event occurred. For example, if a descriptive analysis tells you that 5% of sales dropped, a Diagnostics Analysis will tell you the root cause of the 5% drop in sales.
2. Predictive Analysis
As the name implies, Predictive analysis is about predicting what is likely to happen in the future of a given problem. Predictive analysis is very beneficial for business planning. Predictive Analysts create a model and use the relationship between set variables to make predictions. For Example; Predictive Analysts may predict a correlation between off-season and a drop in sales. Hen based on this business may spend less on advertising campaigns and so on.
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3. Prescriptive Analysis
Prescriptive Analysis looks into what happened why it happened and what is the best course of action that can be taken to avoid the problem in the future. Google Maps serves as the best example here. It considers all the best possible routes to reach a point by all modes of transport.
4. Statistical Analysis
Statistical Analysis looks to answer the question – What Happened? This type of analysis comprises data collection, interpretation, and then a presentation on the dashboard. Statistical Analysis is further divided into Descriptive, Inferential, and Text Analysis
a) Descriptive: Here Analysts summarize numerical data by means, deviations, and percentages in the form of categorical data.
b) Inferential: This involves working with samples from given data. Analysts come to different conclusions from the set of data by choosing different samples.
c) Text: Text Analysis is also called Data Mining and is one of the most direct methods of data analysis. Text Analysis uses databases and data mining tools to know patterns of large datasets. Data Mining or Text Analysis helps convert raw data into useful information.
So What is Data Analytics and Why It Is So Important in Business?
Data Analytics is a process of collecting, cleansing, and visualizing data and drawing conclusions about the results they contain. The techniques used in Data Analytics enable to extraction of raw data and drawing patterns to get insights from it. The importance of Data Analytics is increasing at a rapid speed, today Data Analytics is seen as a crucial part of businesses for planning on resources, making financial decisions, and meeting customers’ expectations.
Data Analytics Helps In:
- Targeting Customers
- Reduced Operational Costs
- Problem-Solving
- Effective Marketing
- Customer Service
- Smooth Operations
For businesses to understand and interpret solutions in a meaningful way, data analysis is crucial. In simple terms, data is the organization, interpretation, structuring, and presentation of information. As organizations become more technologically savvy, they are also becoming more research-driven.
Today, any business that wants to make scientific decisions needs to analyze data. The process is streamlined, money is saved, and customers are offered what they want. Businesses do not want to invest in advertising campaigns and target demographics that have some interest in their products and services.
Data analysis helps businesses to track their product’s performance on targeted demographics. Through data, businesses get a clear picture of targeted audiences’ spending habits and their interests. The data also helps them in setting prices determining the average length of advertising campaigns and some cases even calling off the product.
As a result of data analysis, Coca Coca-Cola beverage giant created the hashtag #Shareacoke to increase customer retention and acquisition, Netflix uses data analysis to target its advertisement campaigns to reach success. Amazon uses innovation to make products.
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Data Analysis Testing Methods
Quantitative Data Analysis is done by working with numerical variables like statistics, percentages, and measurements. It involves using algorithms and mathematical analytical tools to get insights into any value
Techniques of Quantitative Data Analysis
Regression Analysis is a statistical method to predict an outcome of historical data, it helps in understanding and building a model to predict the outcome based on the model. Regression analysis is used to predict behaviors of both dependent and variable factors.
For example, if you want to know what type of Vine people buy, then you would find data on people, their age, financial status, etc who buy Vine. Regression Analysis has two primary types: Simple Linear and Multiple Linear
Simple linear regression analysis is a mathematical representation of a single independent and normally dependent variable. Whereas, In Multiple linear aggression, there are multiple variables with the normal dependent variables. Multiple linear regression models can help in complex relationships of different features and can help to map relationships between multiple variables.
On the other hand, Qualitative Data Analysis is mostly used by businesses. This approach is non-numerical and works with certain identifiers such as labels, and statistical percentages. Quantitative Data Analysts may use interviews, observation, and review documents to get a result.
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Techniques of Qualitative Data Analysis
The Qualitative data analysis is made on two main approaches: Inductive and Deductive Approaches. The inductive approach refers to the collection of data from general observations to conclude with a theory. This type of method is also called the Bottom-up approach as it involves building a set of ideas from a specific view.
In the deductive approach, a researcher is drawn to achieve an accurate answer given that the results are established. There is no possibility of any invalid conclusion if no mistakes were made.
Excel, SQL, Tableau, or Python – Which Technology Do I Learn First?
Many learners have this question in mind when they hear of tech knowledge required for any particular course they want to start with. Learning Excel is one of the best ways to get started in the field of Data Analytics and Excel is such a software that many are intermediate level. Many employers still run their entire data analytics through Excel. So, job opportunities for skilled Excel data analysts are also there.
If you know Excel then learning SQL is a good decision. SQL is a query language for databases like MySQL, and PostgreSQL, online data analysis courses are more focused on syntax and data modeling. Many businesses store their company data in SQL databases and appoint an analyst to extract, summarize, and create a report.
After you have learned about SQL and got some spreadsheet experience, you can move on to learn R. R – Ruby language program is used for statistical computing, scientific research, Data Analytics, visualization, and presentation of data. It is more object-oriented. It emphasizes quality reporting for visualizations and creating interactive web applications.
The R language is designed for Data Analysis and is important to have good knowledge if you want to have a career in data science. However, there are certain drawbacks in R such as slow performance and lack of testing features are some of the reasons that most data scientists and analysts prefer to look for other languages such as Python.
Python is a powerful and versatile language used for various computer tasks like building websites, automating tasks, and doing data analysis. Its flexibility for beginners has made it one of the most used planning languages today. Python is used in the fields of Artificial intelligence, Web development, Mobile application, and Game development.
It highly depends on your goals to learn there are several factors to consider before you start. SQL is interesting to learn because its paradigm is different. SQL robustly handles data. For Example, SQL can handle huge data and can at the same time handle crashes and keep the database system still in a consistent state.
For Data Analysis, you follow a process where you need to gather raw data, and clean and build algorithms. You will then visualize and create a report. To manage data, as a first tool, you’d require SQL or NoSQL databases. NoSQL has gained popularity with big data like the data managed by Google, and Facebook.
Given the current situation of the world, businesses are becoming more data-oriented, hence getting to know SQL is very useful. On the other hand, for job prospects, the best would be Python then SQL, and then R.
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What Does A Data Analyst Do?
Data Analysts are professionals who help businesses in making better decisions to generate good revenues. Data Analysts are responsible for extracting data from databases, excel sheets, and CSVs applying statistical analysis, and converting them to readable formats in the form of summary reports and dashboards.
If Data analysis seems right up to your next career skill, then keep on reading for the best online Data Analysis Courses with certificates.
The listed 5 online data analytics courses will teach you everything you need to know about the job title Data Analyst. Did I mention that these courses will cost you less than the cost of a traditional degree? So, if you don’t have a degree, you can still become a Data Analyst by taking online data analytics courses from a trusted provider.
Best 5 Online Data Analytics Courses
Rank# 1. Online Data Analytics Courses
1. IIM SKILLS
The Master Data Analytics Course provided by the world-renowned institute IIM SKILLS is the most comprehensive training program that is designed to cater to students who are keen to make a career in data analytics. It is a 6-month course which will take you through the most basic to advanced levels of concepts.
They have a complete delivery plan which is scheduled as per the student’s needs and requirements, which makes them feel fulfilled and skilled. Their 24/7 support system is a big help to their student which makes them feel free to reach them and ask their questions.
Why they are called industry leaders is they know what queries the students have. Because of their expertise in data analytics, they cover all of those in the sessions, so in the end, students will be fully satisfied. Still, if you have any queries, you are free to reach them.
Online Data Analytics Courses
It is a government-recognized course that lasts up to 6 months during which you are going to learn the core concepts of data analytics and will be working on different projects to get hands-on practice. It is a tool-driven course, and a variety of courses are covered in it.
It is a must-course for anyone who is truly into building skills as the 2 months of internship followed by the course helps to develop advanced skills and build your portfolio to make you ready to crack any job interview. They have a dedicated placement cell which is a great source of help for the students. All you need to do is trust IIM SKILLS and work hard.
Also Read: IIM SKILLS Data Analytics Course Review
Core Modules:
Data Analytics Using Advanced Excel (9 Hours)
Data Analytics Using VBA (15 Hours)
Data Analytics Using SQL (39 Hours)
Data Analytics Using Power BI (24 Hours)
Data Analytics Using Python-1(10 Hours)
Data Analytics Using Python-2 (10 Hours)
Data Analytics Using Tableau (15 Hours)
R For Data Science (20 Hours)
Data Analytics Using Alteryx (18 Hours)
Contact Details:
Phone: +91 9580 740 740
Email: info@iimskills.com
Rank# 2. Online Data Analytics Courses
2. Google
| Tech | Ratings | Course Fee | Level | Duration | Platform |
| Google Sheets and SQL | 4.8 | Free-$ 49/ Month | Beginner | 6 months | Coursera |
If you are a beginner, this course broadens your knowledge of Data Analytics. The course is highly rated by millions of learners. The entire content of this course is organized and easy to understand. You can actively interact with the active community for any queries that you might have throughout the course. The course will teach you how to use Data Analytical tools like SQL, R, and Tableau by which you will learn the skills to create your projects.
Syllabus of Online Data Analytics Courses:
The course is divided into 8 sub-courses, each focusing on using different statistical tools. If this one sounds interesting, keep on reading its syllabus;
Course 1: Foundations of Data
- Introduction to Data Analytics and
- SQL and Sheets
Course 2: Ask Questions to Make Data-driven Decisions
- Problem-solving
- Asking questions
- Basics of Spreadsheets
- Effective communication
Course 3 Preparing Data for Exploration
- Data Types, Formats, and Models
- Ethics of Data
- Using spreadsheets for Databases
- Introduction to Big Query
- Data Security
Course 4: Cleansing Data from Dirty to Clean
- Data Integrity
- Using SQL and spreadsheets to clean data
- Resume/ career info
Course 5: Analyze Data Answer Questions
- Format and transform Data
- Data Aggregation in Sheets and SQL
- Learn about Tables, functions, and formulas.
- Intermediate level SQL
Course 6: Art Visualization of Data
- Introduction to data visualization concepts
- Creating visualizations with Tableau
- Develop Data stories
- Creating presentations
Course 7: Data Analysis with R Programming
- R language and R Studio
- Transform Data to R
- Creating Visualizations with R
- Making reports and docs from R analyses
Course 8: Google Data Analytics Capstone: Case Study
Developing your capstone project displaying your portfolio and resume
- Information on building a compelling portfolio
Though the entire course completion time is 6 months, the contents can be learned in a shorter time. Many learners completed the entire course in 30 days. It all depends upon your background. Some may finish it in less time while others may take some time.
Rank# 3. Online Data Analytics Courses
3. IBM
| Tech | Ratings | Course Fee | Level | Duration | Platform |
| SQL, Excel, Python, and Cognos Analytics | 4.6 | $39 /Month
|
Professionals | 11 Months | Coursera |
If you are a working professional in Data Analytics and want to get job-ready with professional skills then this is your slot. This course consists of 9 courses and is instructed by IBM professionals, and it comes under the top 5 online data analytics courses. The course will teach how to develop data analysis tools for real problems in businesses.
Using SQL, Python, and Cognos Analytics learners will be able to cleanse, filter, and build advanced data dashboards and develop machine learning models. At the end of the course, learners will be working on a capstone project.
Rank# 5. Online Data Analytics Courses
4. Skillshare
| Tech | Ratings | Course Fee | Level | Duration | Platform |
| SQL
|
4.9 | $13.99 /Month
|
Beginner to Intermediate | 5 hours 4 minutes | Skillshare |
Their online data analytics courses are focused on using SQL language to solve analytical problems with databases. Learners will be able to use SQL on projects.
The Online Data Analysis Courses Cover the Following Topics:
- Introduction to Databases
- Categories of SQL
- Aggregate data
- Entity Relationship Diagrams (ERD)
- Extract transform and Load Data (ETL)
- Introduction to Sales projects
- Creating databases
- Query flow
- Kanban Boards
- Project Management (Agile Methodology)
- Set up SQL
- Exporting results
- Creating a report in Microsoft Excel
Rank# 5. Online Data Analytics Courses
5. Data Analyst Nanodegree
| Tech | Ratings | Course Fee | Level | Duration | Platform |
| SQL and Python | 4.7 | $399 /Month
|
Beginner to Intermediate | 38 hours | Udacity |
This Data Analysis course from Udacity is for learners having basic knowledge of SQL (Num Py and Pandas) and, Python. It’s a great course to learn how to work on large datasheets by having hands-on projects from around the world. The duration of the course is 4 months, and it covers statistical tools such as R, Python, and Tableau.
Online Data Analysis Courses Syllabus
- Introduction to Data Analysis
- Practical Statistics
- Data Wrangling
- Data Visualization with Python
Every course on this list comes with a certificate after the course. Certificates are a good way to prove to yourself that you have completed a course and gained knowledge on that topic. Learners lacking past job experience should focus on developing their portfolio, knowledge, and aptitude.
FAQs
1. Are Data scientists and Data analysts the same?
Answer: Both Data Scientists and Data Analysts work on the same data. The only difference is Data analysts work on already established data to get insights while Data Scientists create new data with the help of scientific tools to solve complex level problems.
2. What is career growth after doing online data analytics courses?
After doing online data analytics courses, you can start working as a professional data analyst. Starting with the position of Data Analyst, you can move to Senior Analyst then Analytics Manager, and then Director of Analytics.
3. Is getting a certificate in Data Analytics worth it?
Ans; Yes, it is. Having a certificate in Data Analytics is a good way to prove to the prospective employer that you have completed a course and gained knowledge on that topic. You can opt for any of the above-mentioned online data analytics courses.
4. Who uses Data Analytics?
Answer; Data Analytics is now used in every industry. Regardless of a company’s size, data analytics is playing a huge part in decision-making processes understanding customer needs, and improving its products in a way that best suits its customers.
