Data Analytics is one of the processes carried out in organizations to collect data from various sources to find trends and patterns along with growth opportunities and risks to make informed decisions and improve the performance of the business.
There are many steps involved in the process including sorting data sources, cleaning, organizing, analyzing, and reporting.
There is a high demand for courses like the Google Data Analytics Certification Course as companies require and hire data analysts to improve their businesses. I also believe that every organization wants to continually improve and get on the top of the leaderboard. Hence, the recruitment of data analysts and implementation of data analysis.
Now, let’s learn about the Google Data Analytics Certification Course. If you choose to join the Google Data Analytics Certificate program, be prepared to give your 100% in learning the concepts and become a data professional. The first thing you need to note is that the course is hosted on Coursera.
Utilize the platform to obtain Google certification. The program will enable you to grasp the required skills and knowledge to enter the ever-growing field.
You will learn to use data to derive insights and draw conclusions from them. Choosing this course will help you save time, energy, money, and effort. Ask me how? This is an online learning program.
It means you can learn the concepts from the comfort of your home, you don’t have to spend money on traveling and exhausting your energy on the commute.
You will get hands-on experience from the training provided by the tutors. This course is for students from two categories; individuals on a job hunt and current data professionals who want to upskill themselves. Let us first look at the former option.
If you are a student who is doing a bachelor’s to gain subject knowledge from your lecturers or you are a degree holder with no experience in data management, make sure to follow the below content.
Applying to a data analytics certificate program will make you eligible for an entry-level job in data analytics. The trainers will expose you to data types, data structures, utilization of data for solving problems, ways to analyze data, data visualization, data storytelling, and usage of R programming to enhance your analysis.
Course Curriculum
- Data Foundations
- Sessions on data-driven decision-making
- Data Preparation for Exploration
- Data Processing
- Data Analysis
- Data visualization
- Use of R Programming in Data Analysis
- Case study on data analytics
Post-completion of the course, you can apply to the below roles without hesitation.
- Data analyst
- Operations Analyst
- Business systems analyst
- Data science analyst
- Business intelligence analyst
- Business intelligence engineer
- Business intelligence developer
Then comes the Advanced Data Analytics Certificate program for individuals with experience in the relevant field. Advanced data scientists and data analysts are assigned to work that involves the collection, analysis, and interpretation of data. If you belong to this category and join this course, you will learn
- Regression analysis
- Python
- Translating insights and data
- Power of statistics
- Machine learning
You will gain a range of skills from the Google Data Analytics Certification Course. Some of them are data cleansing, Data Analysis, Data Visualization, Data Aggregation, R Programming, Data Calculations, Data Ethics, Data Collection, Questioning, Decision-Making, Data Integrity, Sample Size Determination, Presentation, Tableau Software, and Problem-Solving.
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The Google Data Analytics Certification Course will give you the opportunity to deeply understand processes and practices carried out by a junior data analyst. As mentioned above, you can develop the ability to efficiently organize data and perform data analysis using data calculations with the help of R programming, SQL, and spreadsheets.
You will become efficient in presenting and visualizing data findings with the use of key visualization platforms.
Course Highlights
- Shareable Certificate
- Subtitles for a better understanding of the concepts
- Portfolio-ready projects
- Industry-recognized certificate
- Resume review
- Interview Preparation
- Career Support
- Opportunity to connect with Adobe, Wells Fargo, and many more hiring partners of Google
Course duration: 6 months (self-paced)
Course Fees: Free
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Career prospects after completion of the Google Data Analytics Certification Course
Now that we learned about the course details. Let’s take a deeper look at the career prospects for individuals who complete the Google Data Analytics Certification Course.
- Data analyst
- Operations Analyst
- Business systems analyst
- Business intelligence analyst
- Business intelligence engineer
- Business intelligence developer
Data Analyst:
An individual with the responsibility to collect, clean, and interpret data to obtain answers to a question is a data analyst. This person can be a part of any industry; business, criminal justice, government, medicine, or finance.
Questions like “Which age band attracts a specific disease” and “What features in a product can substantially increase the sales numbers” are usually given to data analysts so that they can utilize their skills to perform analysis and answer them.
Microsoft Excel, Google Sheets, SQL, Tableau, Python or R, SAS, Jupyter Notebooks, and Power BI are some of the common tools used by a data analyst.
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Common Responsibilities and Tasks
Below is a list of common activities performed by a data analyst on a daily basis.
- Gather data: Analysts are left to collect data by themselves; conducting surveys, buying datasets, or tracking customer types from specialists in data collection.
- Clean data: Raw data contains errors, outliers, and duplicates in most cases. Data cleaning involves the usage of spreadsheets to maintain the quality of data or the use of a programming language to ensure your interpretations are accurate.
- Model data: Modeling data is nothing but the creation and design part of database structures. If you are a data analyst, you will select data to collect and store it alongside establishing relationships between data categories and working on them.
- Interpret data: Data interpretation is identifying trends or patterns in data to find an answer to the question.
- Present: Communicating your conclusions is as important as your data analysis. It is done by using reports, presentations, and visualizations such as graphs and charts together.
Operations Analyst:
An employee who is hired to solve a company’s internal problems and implement strategies to achieve its goals is called an operations analyst. The individual is given the responsibility to manage data, send reports to clients, and process trades.
If you become an operations analyst, you will be expected to work with the operations team and manager of client support services. Your crucial task is to maintain the smooth running of operations in the company. It feels great to be an operations analyst for two reasons, one – higher compensation, and two – challenging and interesting work.
Common Responsibilities and Tasks
- Identify problems: The primary task of you is to identify potential concerns and operational issues. You must communicate the issues to clients and stakeholders. The picked issues can be related to logistical constraints. For example, you may be asked to determine cost and staffing problems in production and supply.
- Research issues: The following step after the identification of problems is resolving them using suitable modeling strategies. There are many software programs to perform statistical modeling and data analysis. This phase requires you to predict the outcomes for presented solutions. Your job is to find the right solution based on the predicted results.
- Recommend solutions: Use your analysis and research to create reports and make presentations to support the chosen solutions to the identified concerns and problems. You will be given time to prepare detailed reports explaining the effects of implementing the chosen solution along with the drawbacks of implementing it. You need to possess strong communication skills to clearly explain your ideas to others who do not have complete knowledge about the subject.
- Team Collaboration: As a matter of fact, it is not easy to handle all projects by yourself. This is why you will be part of a team where fellow analysts support each other and managers take the final call to implement the solutions. All you have to do is be a reliable and strong team player.
Business Systems Analyst:
Professionals who are recruited by an organization to create strategies based on their data analysis to improve business performance are called business systems analysts.
Big data, cloud-based solutions, machine learning, and artificial intelligence have increased the need for companies to search for business system analysts with the ability to understand and manage complex information systems.
Business systems analysts will have the task of detecting the business needs and making designs for solving problems and improving efficiency.
If you are that one person who is desperately in search of a challenging and rewarding job, then you must consider a business systems analyst role.
Business systems analysts are known for carrying out systems analysis to define specifications and requirements to implement computed-based solutions.
As a business systems analyst, you will interact with users, senior managers, or project team members to create or amend existing hardware configurations and software systems to fulfill organizational needs.
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Common Responsibilities and Tasks
A business systems analyst will have many responsibilities, out of which a few are
- Analysis of crucial data like customer needs
- Develop functional requirements
- Design solutions and write technical requirements
- Test and implement technology solutions
- Conduct research
- Gather requirements from stakeholders
- Analysis of existing business systems and processes
- Identify areas for improvement
- Create a procedure for improving current systems
- Assess software and hardware needs to provide recommendations for business improvement
- Recommend best IT practices
- Provide training and support to users
Business Intelligence Analyst:
Business intelligence analysts are professionals who make use of business data and related information to help the business owner make better decisions. They are commonly addressed as BI analysts.
Common Responsibilities and Tasks
- Break key business data: A BI analyst will collect, and remove duplicates/errors from data and analyze it. They usually work on data like sales, market information, customer engagement metrics, or revenue. Sometimes their team leaders may also ask them to program data models and tools to monitor data.
- Interpret the data: One of the key tasks of a business intelligence analyst is to spot areas in data that indicate growth opportunities for a business. For example, you might find trends to suggest when the business should amend the features of a product.
- Share findings: You must share the findings with the concerned team either through data visualization elements like charts and graphs or by submitting detailed reports. You might also have to present data to clients and management. Sometimes, you will be asked to share your recommendations based on your findings to help the business improve.
Business Intelligence Engineer:
A Business intelligence engineer commonly addressed as a BI engineer takes charge of managing systems and processes in a company for the purpose of data analysis. They are also known to train their juniors in using data visualization software or BI tools.
Common Responsibilities and Tasks
- Design and manage BI applications environment and tools
- Develop ETL pipelines to positively impact production
- Code, test, and document data processes and systems
- Build and maintain the reusable test cases and development documentation
- Transfer knowledge about best protocols and practices to co-workers
Business Intelligence Developer:
It is important to understand that the roles and responsibilities of a business intelligence developer will depend on the stream they are working in. If you are a BI developer, developing a BI interface will be your primary responsibility.
The BI interfaces have different tools. The functionality of these business intelligence solutions is industry-specific. You can get a clear picture of what you are expected to do in an organization based on the provided job description.
BI developers have the knowledge about peculiarities in business areas that will allow them to understand business needs and implement appropriate data representation techniques and models.
Common Responsibilities and Tasks
- Establish business needs for tools of BI
- Convert identified needs into technical ones
- Report data modeling and curation
- Participate in data warehouse design
- Create technical documentation for tools of BI
- Documentation of meta-data storage and data warehouse contents
Skills Gained from Google Data Analytics Certification Course
As previously mentioned, a learner who opts for Google Data Analytics Certification Course will get a chance to acquire knowledge on the below subjects.
Regression Analysis:
Regression Analysis: An analysis that is carried out to show the relationship between variables is called a regression analysis. Graphs are usually used in regression analysis. This chosen technique will help you find out the relationship between an independent variable and a dependent variable. Independent variables have an impact on the dependent variable and regression analysis will point at factors that cause change.
Python:
This language is preferred by many professionals as it can be used to create different programs and is not restricted to creating specific types of tasks. This versatility feature has made it popular among many data users.
You can also use this language to develop data visualizations. The best characteristic of this program is that it is simple to learn. No wonder, many professionals from non-programming backgrounds like scientists and accountants use it in their day-to-day work tasks.
You can apply Python in data analysis, machine learning, scripting/automation, web development, software testing & prototyping, and daily tasks.
Translating Insights and Data:
As we have discussed already, data analytics involves collecting data from different sources. The collected data is raw and needs processing. Translating the data will allow the users to have a clear understanding of the numbers and make decisions accordingly.
On the other hand, when data is translated, you will be able to segregate them based on criteria and draw necessary insights that can be sent to the management and clients letting them make decisions accordingly.
Power of Statistics:
As mentioned earlier, individuals who complete the Google Data Analytics Certification Course will understand the power of statistics. Now, let me tell you how statistics is important in data analytics.
The use of statistics will help the data analysis to gain insights into the operations of the business
It is also known to play a key role in feature engineering and data preprocessing.
Statistics helps data professionals in carrying out analysis to understand trends and patterns in data by allowing them to visualize numbers.
Machine Learning:
Machine learning: It is artificial intelligence that is used for making software applications produce accurate outcomes. You as a data analyst must understand that historical data is used in machine learning algorithms to forecast outcomes.
Machine learning is used for tasks related to recommendation engines. Machine learning is also used for spam filtering, business process automation, fraud detection, malware threat detection, and predictive maintenance.
Machine learning is important because it helps enterprises find out trends in customer behavior and patterns in business operations. It is also known to support analysts in developing new projects.
Believe it or not, popular companies like Uber, Google, and Facebook use machine learning as a crucial part of their operations.
FAQs
Q. Which data analytics course is best for me?
This is a generic question but if you are an aspiring data professional with the aim to attain industry-recognized certificates and gain crucial data analytics skills, then you must pick the Google Data Analytics Certification Course. This program will enable you to grasp key data analytical concepts and knowledge that will allow you to enter any well-reputed company and make the best of your career.
That being said, you must consider your personal goals, financial situation, career objectives, time, and efforts to apply to the program. Doing otherwise can pester your professional life.
Q. What can I become if I complete the Google Data Analytics Certification Course?
Joining the Google Data Analytics Certification Course will open doors to a variety of roles of which a few are
- Data analyst
- Operations Analyst
- Business systems analyst
- Business intelligence analyst
- Business intelligence engineer
- Business intelligence developer
Q. What kind of skills can I learn from joining the Google Data Analytics Certification Course?
You will be exposed to many kinds of tools and techniques to handle data through the Google Data Analytics Certification Course. Some of the key elements you will learn from the course are
- Regression analysis
- Python
- Translating insights and data
- Power of statistics
- Machine learning
Conclusion
Many aspiring data professionals are confused about choosing a range of analytics programs. All such learners can now take a break as the Google Data Analytics Certification Course is now available to help them fulfill their career-related dreams. Google Data Analytics Certification Course helps students like you gain a range of skills and confidence to thrive in the data analytics field.
Individuals who complete the course have the opportunity to become
- Data analyst
- Operations Analyst
- Business systems analyst
- Business intelligence analyst
- Business intelligence engineer
- Business intelligence developer
You will also gain important skills from the program such as
- Regression analysis
- Python
- Translating insights and data
- Power of statistics
- Machine learning
