What Is Data Analysis
First things first: what IS data analysis?
To sum up, analyzing data involves going through enormous amounts of unshaped data and deciphering essential information from it. The information obtained from these investigations is incredibly useful for businesses of all sizes when making a decision.
It is important to remember that data analysis and data science are two distinct subjects. Even though data science and its related family member(s) share the same origin, data science is usually much more complex due to the larger amount of programming, developing new procedures, and creating forecasting models involved.
Hereâ€™s an introduction to the data analytics process:
- Define the question or goal behind the analysis: what are you trying to discover?
- Collect the right data to help answer this question.
- Perform data cleaning/data wrangling to improve data quality and prepare it for analysis and interpretationâ€“getting data into the right format, getting rid of unnecessary data, correcting spelling mistakes, etc.
- Manipulate data using Excel or Google Sheets . This may include plotting the data out, creating pivot tables, and so on.
- Analyze and interpret the data using statistical tools (i.e. finding correlations, trends, outliers, etc.).
- Present this data in meaningful ways: graphs, visualizations, charts, tables, etc. Data analysts may report their findings to project managers, department heads, and senior-level business executives to help them make decisions and spot patterns and trends.
Is data analytics hard? Data analysis is a great starting point for newbies as it doesn’t require advanced training. Once someone enrolls in a few beginner-level courses and works on polishing selected abilities, they can start right away. It would certainly be beneficial if you have prior knowledge in coding, mathematics, or statistics.
Pursuing a career as a data analyst can give you the opportunity to advance to high-paying roles such as data science or data engineering when you have obtained more experience.
Types of Data Analysis
What is the key objective of data analysis? What is determined by the data analysis skills that you are utilizing? Here are five kinds of data analytics.
The objective of descriptive analysis is to provide a summary of data which is both informative and descriptive, rather than to make any presumptions. The purpose of this type of analytics is to answer the inquiry, “What occurred?” Examples include monthly revenue reports and KPI dashboards.
Investigating further than descriptive analytics, exploratory analysis looks for any identifiable patterns or trends that may be in the data. This could also be viewed as the preliminary exploration stage.
An examination for diagnosis: Examines the percepts acquired from both descriptive and investigative analytics further to pinpoint the sources.
This type of analysis is generally utilized more frequently by data scientists instead of data analysts. It assesses the probability of possible results in the future through the utilization of data, numbers, and machine learning algorithms and methods. Examples include sales forecasting and risk assessment.
Applying the knowledge gained through descriptive, exploratory, diagnostic, and predictive data analysis, prescriptive analysis can be used to figure out the optimal strategy to take.
Data Analysis Methods
What techniques do data analysts utilize to meet their various goals? Here’s a quick introduction to data analytics methods.
Grouping data together based on shared features is known as cluster analysis. This technique forms clusters which have similar characteristics. Further information about Cluster Analysis and Unsupervised Machine Learning in Python can be found here.
An investigation utilizing regression analysis can be conducted to assess the correlation between two or more variables. You can gain further knowledge about regresison analysis and data analytics connected to regression by visiting this link.
The use of factor analysis simplifies data analysis by enabling the consolidation of multiple variables into only a few. Learn more: An Introduction to Factor Analysis
Exploring large datasets to detect trends, patterns, and correlations. Gain additional knowledge: Expand your understanding of Data Mining with R from Basic to Expert Level!
Obtaining data that can be read by a computer from unstructured text sources (e.g. PDF files, word documents, emails) through text analysis. Investigate further: Investigation of Text Analysis and Use of Natural Language Processing Techniques Using Python
Why You Should Learn Data Analysis Skills
What are the advantages of learning data analysis and pursuing a career in this area? It appears only appropriate that we examine the information to figure it out!
- There is anticipated job growth for data professionals: The anticipated job growth for market research analysts (another term for data analysts) between 2020-2030 is 25%, based on data from the Bureau of Labor Statistics. That’s a significant amount of new positions being created.
- Data analytics is in demand: According to Digital Learning Academy, creators of Introduction to Data Analysis and Statistics Using SQL, “There is a demand for people who can use data to perform reporting and analysis, thus helping businesses and organizations make important and critical decisions.”
- Data roles have higher than average salaries: Data analysts are paid well even if they don’t continue on to data science or engineering! How much do data analysts make? According to Payscale, entry-level data analysts will receive an annual salary between $40,000 – $73,000 (average of $57,000). Senior data analysts can bump that up as high as $83,000.
- There is a competitive advantage: According to Ian Littlejohn, instructor of Complete Introduction to Business Data Analysis, “The ability to ask questions of your data is a powerful competitive advantage, resulting in new income streams, better decision making and improved productivity.”
- Universal need (all kinds of companies require data help): According to Symon He and Travis Chow, instructors of Data Analysis Essentials Using Excel, “Every business generates data. But [its value] depends on your ability to process, manipulate, and ultimately translate that data into useful insights.”
In the end, data analysis is beneficial for both businesses and people. This could be a long-term occupation or it could be utilized as a stepping stone for other data-related roles.
Popular Careers That Rely on Data Analysis
Gaining data analytic capabilities can open up a variety of career paths, not limiting one to a single direction. Definitely, you may decide to be a data analyst for a long period if it makes you happy–but there is also the opportunity to move into other areas if you prefer.
Organizations across multiple fields employ data analytics to drive choices, gain a benefit over the competition, enhance sales, acquire new customers, perfect internal procedures, and augment profits, among other benefits. This renders expertise in data analysis advantageous for a variety of occupations.
Below are some of the top occupations that involve data analysis.
1. Data Analyst
To begin with, let’s take a closer look at what data analyst functions are, to gain a better understanding of performing data analysis.
WHAT IS A DATA ANALYSIS ROLE?
The core steps of data analysis include obtaining and collecting huge amounts of information, arranging it, and transforming it into useful knowledge that companies can apply to make more informed choices and draw results. An analyst or data visualization specialist transforms meaningless data into valuable, usable outcomes to present to business managers. In short, they provide a platform for producing key decisions.
An example of a job a data analyst does is taking amass of information sourced from customer surveys, or reviewing past customer purchases, for example, and cleaning it to be used for analysis. This will then be displayed in reports and visuals to establish ways in which the product of a company, be it an app, vehicle producer, grocery store or any other company, can be refined to generate more revenue.
2. Business Analyst
WHAT DO BUSINESS ANALYSTS DO?
They analyze data to figure out important relationships that can assist in directing business choices, collaborating actively with corporate vice presidents and high-level executives. Their responsibilities can include forecasting, making improvements, controlling risks, etc.
3. Product Manager
WHAT DO PRODUCT MANAGERS DO?
Product managers are responsible for overseeing and ensuring the success of products from the creation stage to when they are released. Each stage requires data analytics! Examine the market for patterns and issues that need tending to, utilize data to figure out how to upgrade characteristics, and decide how to further upgrade the product in future versions.
Best Data Analytics Courses for Beginners
365 Data Science
If you are looking to master data science (especially data analytics) via video tutorials, 365 Data Science is probably the most effective choice.
You can gain knowledge from a group of experienced teachers who have extensive practical expertise from their previous employment at world-renowned firms like Google, Autodesk, and more.
Data Analyst Career Track
This program is developed for learners who aspire to work as data analysts. It consists of 10 courses as follows:
- Introduction to Data and Data Science
- Introduction to Excel
- Introduction to Python
- The Complete Data Visualization Course with Python, R, Tableau, and Excel
- Data Preprocessing with NumPy
- Data Cleaning and Preprocessing with Pandas
- SQL for Data Science Interviews
- Dates and Times in Python
This curriculum offers more than forty-one hours worth of video material which covers all the angles of data analytics, starting with the basics, presented in a sensible sequence. Furthermore, all videos are also bite-sized. Therefore, those who are brand new to the field can move through the career path with no difficulties.
Quizzes and coding assignments are included in every class so that you can hone your abilities. The only downside is that there is a lack of expansive, difficult assignments for pupils to finish. To gain more hands-on experience, you may need to sign up for ProjectPro (view the information provided below).
Once you finish this career path, you can use 365 Data Science’s machine learning and deep learning courses (already incorporated in the membership fee) to broaden your comprehension, granting you an ability to do a more thorough analysis of data.
Pros & Cons
- Learn from veteran industry experts
- Beginner-friendly, easy-to-follow curriculum
- Bite-sized video lessons
- Self-paced learning
- 30-day money-back guarantee
- All-inclusive pricing (Provide complete access to all video lessons + No hidden fees)
- No large-scale projects for students to complete
IBM Data Analyst
This course provides basic education on how to analyze data for those who have no prior experience. You will learn from a team of eight experts. This is a group of experienced data scientists working at IBM.
Pros & Cons
- Learn from the team of experts at IBM
- Comprehensive curriculum
- Self-paced learning
- Bite-sized, beginner-friendly video lessons
- Offer various case studies and hands-on labs for learners to apply concepts through real-life examples
- Free auditing
- Based on actual student reviews, the Python project (Course 5) can be too challenging for absolute beginners who don’t have prior programming experience.
- Some courses have minor quality issues that students may find distracting.
- Not the best course if you prefer not to use IBM tools such as Cognos or Watson
Data Analytics and Visualization with Excel and R
IBM’s program will take you step-by-step through the whole data evaluation and graphical representation procedure. You will be utilizing Excel and R to gain meaningful information and make an appealing narrative to show to your listeners. Python will no longer be used.
The program consists of eight small classes (Courses 1-3 are the same as those on the Python syllabus, so I will begin with Course 4).
4. The fourth course in the Data Science program will go over the essentials of the R programming language. You will be taught about different kinds of data, how data is organized, when to take certain actions, writing code to perform specific tasks, and how to employ R to alter data and extract information from websites.
5. This fifth course will focus on exploring SQL and the ideas related to relational databases. You will compose fundamental SQL commands and utilize them to oversee databases.
Afterwards, you will utilize both R and SQL to search for genuine data sets.
6. Examining Data with R – This lesson will guide you through the basics of data analysis as well as all associated steps.
You will gain knowledge on utilizing R to organize your data, gain a better understanding of your data through exploratory data analysis, and construct and assess R models to single out the essential connections between separate variables.
Pros & Cons
- Learn from the team of data scientists and software engineers at IBM
- Well-structured curriculum
- Most lessons are informative and easy to follow.
- Project-based learning
- Free auditing
- In some lessons, the instructors assume that the learners are already familiar with the basic programming concepts. Unfortunately, many beginners do not. Thus many of them become confused.
- Not the best choice if you do not want to use IBM tools such as Cognos
Leave a Reply