Data Analytics

Overview

The burgeoning field of Data Analytics offers a powerful toolkit for extracting insights and knowledge from the ever-growing ocean of data. This programme equips you with the essential skills and knowledge to navigate this exciting domain and become a valuable asset in the data-driven world. The demand for skilled data analytics is skyrocketing across industries. Businesses of all sizes understand the power of data and are actively seeking professionals who can translate it into actionable strategies.

This comprehensive programme is designed to provide a well-rounded understanding of data analytics, encompassing both theoretical foundations and practical applications.

Craftudy prioritizes a flexible and engaging learning experience. You get to learn from industry professionals through real-time sessions, with access to mentorship, hands-on projects and have your questions answered in real-time, while connecting with fellow learners and instructors through our interactive online forum, fostering collaboration and knowledge sharing.

Programme Requirements

No prior knowledge required: This course is designed for beginners, and you'll develop the necessary technical skills along the way.

What You'll Learn

Data Fundamentals:

Understand the core concepts of data, its different types (structured, unstructured, semi-structured), and data collection methodologies.
Learn about data quality principles and techniques for data cleaning and wrangling to ensure data accuracy and usability.
Explore various data storage solutions like databases (relational, NoSQL) and data warehousing concepts.

Programming for Data Science:

Learn the fundamentals of Python, a powerful programming language widely used in data analytics.
Understand essential libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization.
Explore other relevant tools like scikit-learn for machine learning algorithms and BeautifulSoup for web scraping.

Statistics & Probability:

Brush up on your statistical foundations: descriptive statistics, hypothesis testing, correlation, and regression analysis.
Understand probability concepts like random variables, probability distributions, and central limit theorem.
Learn to apply statistical techniques to analyze data, draw inferences, and make informed decisions.

Machine Learning:

Discover the fascinating world of machine learning algorithms, exploring supervised learning (classification, regression) and unsupervised learning (clustering, dimensionality reduction).
Learn about model evaluation techniques, common pitfalls to avoid (overfitting, underfitting), and model selection strategies.
Practical experience implementing various machine learning algorithms using Python libraries.

Data Visualization:

Learn the art and science of effectively communicating insights through data visualization.
Explore popular data visualization libraries like matplotlib, seaborn, and Tableau to create compelling charts, graphs, and dashboards.
Learn about best practices for visual design to ensure your visualizations are clear, informative, and visually appealing.

Big Data & Cloud Computing:

Understand the challenges and opportunities presented by Big Data, including volume, variety, and velocity.
Explore distributed computing frameworks like Hadoop and Spark for handling large datasets efficiently.
Get insights into cloud computing platforms like AWS, Azure, and Google Cloud Platform for data storage, processing, and scalability.

Data Ethics & Communication:

Develop a critical understanding of data ethics principles: privacy, bias, fairness, and transparency.
Learn how to communicate complex data insights effectively to technical and non-technical audiences.
Explore best practices for data storytelling to present insights in a compelling and impactful manner.

Programme Structure

The course will be delivered online (in real-time).

Course Duration:

12 months

The programme will combine interactive lectures, hands-on coding exercises, real-world case studies, and collaborative projects.
Lectures will provide foundational knowledge and introduce key concepts.
Coding exercises will allow you to apply your programming skills to practical data analysis scenarios.
Case studies will expose you to real-world data analytics problems and industry applications.
Collaborative projects will give you an opportunity to work in teams, tackle a data challenge, and develop your problem-solving and communication skills.

By successfully completing this programme, you will be equipped with:

A solid foundation in data science and data analytics concepts.
Proficiency in Python programming and essential data analytics libraries.
The ability to analyze data, clean and prepare it for analysis.
The capability to apply statistical methods and machine learning algorithms.
Skills to create informative and engaging data visualizations.
An understanding of big data technologies and cloud computing solutions.
A critical awareness of data ethics principles and effective data communication strategies.

This comprehensive course will empower you to embark on a rewarding career in data science or data analytics and become a valued contributor in the data-driven landscape.

Career Prospects

The job outlook for data analysts is excellent. The Bureau of Labor Statistics projects a 33% growth rate for data analyst jobs from 2020 to 2030, much faster than the average for all occupations. This rapid growth is driven by the increasing amount of data being generated by businesses and the need to make sense of it all. By enrolling in this programme, you will open doors to a wide range of exciting career opportunities, including:

Data Analyst

Business Intelligence Analyst

Machine Learning Engineer

Data Engineer

These data analytics skills are highly sought-after across various industries, including Technology (Fintech, Social Media, E-commerce), Finance (Banking, Investment, Insurance), Healthcare & Pharmaceuticals, Retail & Consumer Goods, Manufacturing & Logistics, Energy & Utilities, Media & Entertainment.

Salary Potential

Salary Potential:
The salary potential in data analytics is quite attractive, with factors like experience, location, and specific skillset impacting the earning range. Entry-level data analyst positions might start in the $50,000-$70,000 range, while experienced data scientists can command salaries exceeding $150,000.

Craft and execute data-driven digital marketing campaigns that deliver measurable results.
Reach and engage your target audience across various online channels.
Increase brand awareness, website traffic, and leads for your business.
Stay ahead of the curve in the ever-evolving digital marketing landscape.

Study Support

At Craftudy, we are dedicated to supporting our students in achieving their career aspirations. Our students have access to personalized guidance and resources, including one-on-one mentorship from Career Mentors, who provide expert advice on exploring career options and honing your kills. We integrate career support directly into the curriculum, ensuring that you are well-prepared to embark on your professional journey.
Additionally, our session are in real-time (not prerecorded) and our tutors and help desks are readily accessible, supporting your learning beyond classroom interactions.