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JUMPSTART YOUR NEW CAREER IN DATA SCIENCE
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Certification Courses
Data Science Certification
Data Analytics Training
Data Engineering
Data scientists use a variety of tools and techniques to analyze and interpret data, including data mining, machine learning, statistical modeling, and visualization. They work with large, complex datasets to identify patterns and insights that can be used to inform business decisions, scientific research, and other applications. Some common tasks that data scientists might perform include data cleaning and preprocessing, exploratory data analysis, feature engineering, model training and evaluation, and communication of findings to stakeholders.
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Data Science Training
Data science training is a program that provides learners with the knowledge and skills required to analyze, interpret, and extract meaningful insights from complex data sets. Data science involves a combination of statistical analysis, machine learning, data visualization, and other techniques to make sense of large amounts of data. There are many options for data science training, including online courses, boot camps, and traditional classroom-based programs. The content of a data science training program typically covers a range of topics, such as data wrangling, data exploration, predictive modeling, and communication of insights
Data Science & AI Job Profiles
- Data Science Consultant
- Data Engineer
- Full Stack Data Scientist
- Data Analyst Engineer
- Data Mining & Engineer
- Data base Administrator
- Business Analyst
- Data Architect
- Statistician
- AI Engineer
- Data & Analytics Manager
- M L Engineer
What will you learn?
In a data science training program, you can expect to learn a wide range of skills and concepts related to data analysis and machine learning. The specific topics covered may vary depending on the program and its level, but here are some of the key areas that are typically covered in a data science training program:- Data Exploration and Visualization: Learn how to explore and visualize data using tools like Python, R, and Tableau.
- Statistical Analysis: Learn how to use statistical methods and tools to analyze data and extract insights.
- Data Cleaning and Preprocessing: Learn how to clean and preprocess data to make it suitable for analysis.
- Machine Learning: Learn how to apply machine learning algorithms to solve real-world problems and predict outcomes.
- Deep Learning: Learn how to use neural networks and deep learning algorithms for complex tasks like image recognition and natural language processing.
- Big Data: Learn how to handle and analyze large and complex data sets using tools like Hadoop and Spark.
- Communication of Insights: Learn how to effectively communicate insights and findings to stakeholders using data visualization and storytelling techniques.
Data Science Course objectives:

- Develop a strong foundation in data science: A data science course aims to provide learners with a solid foundation in the concepts and techniques of data science. This includes knowledge of statistics, machine learning, data wrangling, and data visualization.
- Learn to work with real-world data sets: Data science courses often provide learners with hands-on experience working with real-world data sets, which can help learners to develop practical skills and problem-solving abilities.
- Master the use of data science tools and technologies: Data science courses typically cover the use of various tools and technologies used in the field of data science, such as Python, R, Tableau, and Hadoop. By the end of the course, learners should be proficient in the use of these tools.
- Develop critical thinking and problem-solving skills: Data science courses aim to develop learners’ critical thinking and problem-solving skills, as they work through various data science problems and case studies.
Artificial Intelligence course objectives:

- Develop a strong foundation in AI: An AI course aims to provide learners with a comprehensive understanding of the fundamental concepts and techniques of AI, including machine learning, deep learning, natural language processing (NLP), computer vision, and robotics.
- Learn to apply AI techniques: AI courses often provide learners with practical experience applying AI techniques to real-world problems. This includes developing and training AI models using various programming languages and frameworks, such as Python, TensorFlow, and PyTorch.
- Understand the ethical implications of AI: AI courses aim to help learners understand the ethical implications of AI and its impact on society. This includes issues related to bias, fairness, privacy, and accountability.
- Develop critical thinking and problem-solving skills: AI courses aim to develop learners’ critical thinking and problem-solving skills as they work through various AI problems and case studies.
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LEARNING MODES

Classroom & Bootcamp
Classroom and boot camp are two different formats of learning environments. A classroom is a traditional setting where students attend in-person classes with a teacher.
Interactive Live online
Interactive live online refers to a type of online learning or communication that takes place in real-time and involves active participation and engagement from all participants.

Blended eLearning
Blended eLearning, also known as hybrid learning or mixed-mode learning, is an educational approach that combines traditional classroom instruction with online learning activities.

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