What is Data Analytics?
Data analytics is the process of examining raw data with the purpose of drawing conclusions about that information. It involves the application of various techniques and tools to transform data into meaningful insights, which can then be used to inform decision-making and drive business strategy.
Data analytics encompasses a wide range of activities, including data collection, cleaning, preparation, analysis, interpretation, and visualization. It involves extracting and analyzing data from various sources such as databases, sensors, social media, and other platforms.
The data collected can be structured (organized in a specific format like databases) or unstructured (text documents, multimedia files).
Scope
The goal of data analytics is to extract actionable insights from data that can be used to drive decision-making, optimize processes, improve efficiency, enhance customer experiences, and gain a competitive advantage. With the proliferation of big data and advancements in technology, the importance of data analytics continues to grow, shaping the future of businesses and organizations across various industries.
Overall, data analytics has become indispensable in today’s digital age, empowering organizations across industries to extract value from data, drive innovation, and achieve their business objectives more effectively.
Unlike Data Science where more in depth technology learning is involved, Data Analytics focusses on data analysis and visualization.
Job profiles in Data Analytics
Completing this job oriented data analyst course from Easy Courses can open up a wide range of career opportunities in various industries. Here are some of the potential career paths and job roles you could pursue after completing a data analytics course:
- Data Analyst
- Business Analyst
- Data Scientist
- Data Engineer
- Data Visualization Specialist
- Quantitative Analyst
These are just a few examples of the many career paths available in data analytics. Depending on your interests, skills, and career goals, you can explore various roles and industries where data analytics skills are in high demand.
With the growing importance of data-driven decision-making, there are abundant opportunities for individuals with expertise in data analytics to make a significant impact in their chosen fields.
Hiring Companies
Industry Trends
The global market for big data analytics is poised for substantial expansion, with its value projected to $745.15 billion by 2030.
Statista reports that in 2023, approximately 60% of 116 surveyed businesses indicated they were leveraging data to drive innovation.
There will be 11.5 million new job openings in data science and analytics by 2026 (US bureau of Labour and Statistics)
By 2026, 80% of organisations will implement multiple data hubs (Gartner)
Who can Learn Data Analytics Course?
Anyone with an interest in working with data and a willingness to learn can pursue a career in data analytics as a Data Analyst or Business Analyst. Here are some examples of individuals who can benefit from learning data analytics:
- College Students
- Working Professionals
- Career Changers
- Entrepreneurs and Business Owners
- Freelancers and Consultants
- Researchers and Academics
- Enthusiasts and Hobbyists
Key Differentiators
Easy Courses differentiates itself from others in several ways to stand out in the competitive market. Here are some of those points for your ready reference.
Specialization: Focus on this specific industry segment within the field of technology training which makes us an expert in this domain.
High-Quality Content: Develop and deliver high-quality training content that is comprehensive, up-to-date, and relevant to industry dynamics. Invest in experienced instructors, subject matter experts, and instructional designers to ensure the content is engaging, informative, and effective.
Interactive Learning Experience: Offer interactive and engaging learning experiences that go beyond traditional lectures or presentations. Incorporate hands-on exercises, case studies, simulations, and real-world projects to enhance learner engagement and retention.
Flexible Learning Options: Provide flexible learning options to accommodate the diverse needs and preferences of learners. Offer both in-person and online training formats, self-paced and instructor-led courses, as well as blended learning approaches that combine online and offline components.
Personalized Learning Paths: Offer personalized learning paths or customized training programs to cater to the specific goals, skill levels, and learning styles of individual learners or organizations. Provide assessments, pre-tests, or skills gap analyses to tailor the training content and pace to each learner’s needs.
Industry Partnerships: Forge strategic partnerships with industries and technology vendors to enhance the credibility and relevance of the training programs. Collaborate with industry experts or guest speakers to provide insights and perspectives from the field.
Certifications and Credentials: Offer recognized certifications, credentials for completing training programs. Partner with certification bodies or industry leaders to provide certification exams or pathways that add value to the learners’ resumes and career prospects.
Career Services and Support: Provide additional support services such as career counseling, job placement assistance, resume writing workshops, or networking opportunities to help learners transition into new roles or advance their careers after completing the training programs.
Continuous Improvement: Continuously monitor and evaluate the effectiveness of training programs through learner feedback, performance metrics, and industry benchmarks. Regularly update and improve the content, delivery methods, and learning resources to ensure relevance and quality.
Customer-Centric Approach: Focus on building strong relationships with customers and learners by delivering exceptional service, responsiveness, and support throughout the learning journey. Listen to customer feedback, address their concerns promptly, and strive to exceed their expectations.
Modules / Syllabus
- Introduction to data analytics / Statistical analysis
- Python language
- Matplotlib | pandas
- Databases: SQL
- Data cleansing and transformation: advanced excel
- Data cleansing and transformation: power query
- Dax
- Data visualization: power bi
- Power query editor
- Modeling & reporting
- Data Analysis through Python
Methodology
Easy Courses follow specific training methodologies for Business Analytics course that caters to diverse learning styles of participants while ensuring comprehension, retention, and practical application of concepts. Here are some methodologies that can be employed:
- Lecture-based Learning
- Hands-on Practice
- Case Studies and Real-world Projects
- Group Work and Collaboration
- Feedback and Assessment
- Mentorship and Coaching
- Continuous Learning and Resources
