Simple Explanation: The art of generating meaning from data and using it to solve real world problems.

Detailed Explanation: Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms to extract knowledge and insights from many structural and unstructured data.

The goal of data science is to analyze data to uncover patterns, trends, and correlations that can inform decision-making, predict future outcomes, and solve real-world problems.

Data scientists use a variety of techniques and tools to work with data, including statistical analysis, machine learning, data mining, and data visualization. They often employ programming languages like Python, R, or SQL to manipulate and analyze data sets, as well as tools like Jupyter Notebooks, TensorFlow, and scikit-learn.

In essence, data science encompasses the entire process of acquiring, cleaning, analyzing, and interpreting data to extract valuable insights and drive informed decision-making in various fields such as business, healthcare, finance, marketing, and more.

• A laptop/ computer (Preferably with 8GB RAM or more) with stable internet connection
• Possess basic computer skills
• Be eager to learn
• Be available for classes

• Anyone who is computer literate, understands English and is eager to learn.

• Friendly trainers
• Anyone can be our student
• Flexible payment plan
• Discounted fees
• Multiple delivery methods (On-site & Virtual)
• Heavily hands-on/ practical
• Access to our mentorship network
• Internationally recognized certification
• Access our other courses using alumni discount code

• Skill Development: Data science courses provide structured learning paths to acquire essential skills in data analysis, statistics, machine learning, and programming languages like Python or R.

• Career Opportunities: Data science is a rapidly growing field with high demand for skilled professionals. Completing a data science course can enhance your job prospects and open up opportunities in various industries such as tech, healthcare, finance, and e-commerce.

• Problem Solving: Data science involves extracting insights from data to solve complex problems. By taking a course, you'll learn how to tackle real-world challenges using data-driven approaches, which can be applicable across different domains.

• Stay Updated: The field of data science is constantly evolving with new tools, techniques, and technologies. Enrolling in a course helps you stay updated with the latest trends and developments in the field.

• Networking: We offer opportunities to connect with instructors, peers, and industry professionals. Networking can be valuable for sharing knowledge, exploring job opportunities, and building a community of like-minded individuals.

• Personal Development: Even if you're not pursuing a career in data science, learning data analysis skills can be valuable for personal projects, research, or improving decision-making in your current role.

1. Data Scientist: Data scientists are responsible for collecting, analyzing, and interpreting large datasets to extract actionable insights and solve complex problems. They utilize techniques from statistics, machine learning, and data mining to uncover patterns and trends in data.
2. Data Analyst: Data analysts focus on examining data to identify trends, develop reports, and provide insights to support business decision-making. They often work with structured data using tools like SQL, Excel, and visualization software.
3. Machine Learning Engineer: Machine learning engineers design, build, and deploy machine learning models and systems that can make predictions or decisions autonomously. They work with large datasets and implement algorithms using programming languages like Python or R.
4. Business Intelligence Analyst: Business intelligence analysts gather and analyze data to provide insights into business operations, market trends, and customer behavior. They develop dashboards, reports, and data visualizations to communicate findings to stakeholders.
5. Data Engineer: Data engineers design and maintain the infrastructure needed to collect, store, and process large volumes of data. They build data pipelines, manage databases, and ensure data quality and accessibility for analysis.
6. Data Architect: Data architects design the overall structure of databases and data systems to ensure they meet the organization's needs for data storage, integration, and accessibility. They collaborate with stakeholders to define data requirements and develop data models and schemas.
7. AI Research Scientist: AI research scientists conduct research and develop new algorithms, models, and techniques to advance the field of artificial intelligence. They explore areas such as natural language processing, computer vision, and reinforcement learning.
8. Quantitative Analyst (Quant): Quants apply mathematical and statistical methods to analyze financial data, develop trading strategies, and manage investment portfolios. They work in finance, investment firms, and trading companies.
9. Data Science Consultant: Data science consultants provide expertise and guidance to organizations on leveraging data for business insights and decision-making. They analyze data, develop custom solutions, and help implement data-driven strategies.
10. Academic Researcher: Academic researchers in data science work in universities and research institutions to conduct studies, publish papers, and advance knowledge in areas such as machine learning, data mining, and computational statistics.

A technical mentor is someone who is an expert or has extensive knowledge in that field and is there to provide guidance, support in that specific field.

In this field of Data Science, a technical is one wo has experience as a data analyst, data scientist, data science teacher, AI engineer, data engineer and much more.