all.in.one: AI & DATA SCIENCE
470+ Courses | 4655+ Hrs | 80+ Specializations | Tests | Certificates
Embark on a transformative journey through our comprehensive course bundle, delving into core areas like Machine Learning, Artificial Intelligence, and Deep Learning. Explore Predictive Modelling, Business Intelligence, and Data Science, and master the art of Data Visualization. Dive deep into Big Data, IoT, Cloud Computing, and SalesForce, leveraging a diverse array of software platforms and tools including R, Python, SAS, Power BI, and Tableau. Unleash your analytical prowess with SPSS, Minitab, AWS, Informatica, eviews, Matlab, and more, as you navigate the landscape of Statistical Analysis and beyond.
Offer ends in:
What you'll get
- 4655+ Hours
- 470+ Courses
- 80+ Specializations
- Mock Tests
- Unlimited Certificates
- Unlimited Access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
- Download Curriculum
Skills you will master
- Machine Learning: Acquire expertise in algorithms and techniques for predictive modeling and pattern recognition.
- Artificial Intelligence: Develop advanced understanding and applications of AI principles for intelligent systems.
- Deep Learning: Master neural network architectures and frameworks for complex pattern recognition tasks.
- Predictive Modeling: Learn to build models that forecast future trends and behaviors based on past data.
- Business Intelligence: Gain proficiency in tools and techniques for analyzing and visualizing business data to drive insights.
- Data Science: Acquire skills in data manipulation, analysis, and interpretation for solving complex problems.
- Data Visualization: Learn to create compelling visualizations that effectively communicate insights from data.
- Big Data Analytics: Develop capabilities to process and analyze large and complex datasets efficiently.
- Internet of Things (IoT): Understand IoT concepts and applications for collecting and analyzing data from connected devices.
- Cloud Computing: Learn to leverage cloud platforms for storage, processing, and analysis of data at scale.
- SalesForce: Master the SalesForce platform for customer relationship management and sales automation.
- Statistical Analysis: Develop expertise in statistical methods and techniques for analyzing and interpreting data.
- R Programming: Acquire proficiency in R programming language for statistical computing and graphics.
- Python Programming: Learn Python programming language for data manipulation, analysis, and visualization.
- SAS: Gain skills in SAS software for statistical analysis, data management, and predictive modeling.
- Power BI: Learn to use Power BI for data visualization, interactive reporting, and business intelligence.
- Tableau: Master Tableau for creating interactive and shareable dashboards and visualizations.
- SPSS: Acquire proficiency in SPSS software for statistical analysis and data mining.
- Minitab: Learn to use Minitab for statistical analysis, quality improvement, and process optimization.
- AWS: Develop skills in Amazon Web Services for cloud computing, storage, and analytics.
- Informatica: Gain expertise in Informatica for data integration, data quality, and master data management.
- eViews: Learn eViews for econometric analysis and forecasting in economics and finance.
- Matlab: Acquire skills in Matlab for numerical computing, data analysis, and visualization.
- Hadoop: Master Hadoop for distributed storage and processing of big data sets across clusters of computers.
- Microsoft Power BI: Develop proficiency in Microsoft Power BI for business analytics and data visualization.
- Google Analytics: Learn Google Analytics for web analytics, tracking, and reporting of website traffic.
- TensorFlow: Gain expertise in TensorFlow for building and training deep learning models.
- Apache Spark: Acquire skills in Apache Spark for big data processing and analytics.
- PySpark: Learn PySpark for parallel data processing with Apache Spark using Python.
- SQL: Develop proficiency in SQL for managing and querying relational databases.
- Ansible: Acquire skills in Ansible for automating IT infrastructure tasks such as application deployment and configuration management.
- Splunk: Learn Splunk for operational intelligence, log management, and real-time data analytics.
- Pandas & NumPy: Master Pandas and NumPy libraries for data manipulation and numerical computing in Python.
- Seaborn: Develop proficiency in Seaborn for statistical data visualization in Python.
- Cassandra: Acquire skills in Cassandra for distributed database management and scalable, high-performance applications.
- Octave: Learn Octave for numerical computing and data analysis, particularly for scientific and engineering applications.
- Scikit-learn: Master Scikit-learn library for machine learning algorithms and model building in Python.
- Talend: Develop proficiency in Talend for data integration, data quality, and big data management.
- OpenCV: Acquire skills in OpenCV for computer vision and image processing tasks.
- QlikView: Learn QlikView for data visualization, business intelligence, and interactive dashboards.
Content
- Machine Learning: Acquire expertise in algorithms and techniques for predictive modeling and pattern recognition.
- Artificial Intelligence: Develop advanced understanding and applications of AI principles for intelligent systems.
- Deep Learning: Master neural network architectures and frameworks for complex pattern recognition tasks.
- Predictive Modeling: Learn to build models that forecast future trends and behaviors based on past data.
- Business Intelligence: Gain proficiency in tools and techniques for analyzing and visualizing business data to drive insights.
- Data Science: Acquire skills in data manipulation, analysis, and interpretation for solving complex problems.
- Data Visualization: Learn to create compelling visualizations that effectively communicate insights from data.
- Big Data Analytics: Develop capabilities to process and analyze large and complex datasets efficiently.
- Internet of Things (IoT): Understand IoT concepts and applications for collecting and analyzing data from connected devices.
- Cloud Computing: Learn to leverage cloud platforms for storage, processing, and analysis of data at scale.
- SalesForce: Master the SalesForce platform for customer relationship management and sales automation.
- Statistical Analysis: Develop expertise in statistical methods and techniques for analyzing and interpreting data.
- R Programming: Acquire proficiency in R programming language for statistical computing and graphics.
- Python Programming: Learn Python programming language for data manipulation, analysis, and visualization.
- SAS: Gain skills in SAS software for statistical analysis, data management, and predictive modeling.
- Power BI: Learn to use Power BI for data visualization, interactive reporting, and business intelligence.
- Tableau: Master Tableau for creating interactive and shareable dashboards and visualizations.
- SPSS: Acquire proficiency in SPSS software for statistical analysis and data mining.
- Minitab: Learn to use Minitab for statistical analysis, quality improvement, and process optimization.
- AWS: Develop skills in Amazon Web Services for cloud computing, storage, and analytics.
- Informatica: Gain expertise in Informatica for data integration, data quality, and master data management.
- eViews: Learn eViews for econometric analysis and forecasting in economics and finance.
- Matlab: Acquire skills in Matlab for numerical computing, data analysis, and visualization.
- Hadoop: Master Hadoop for distributed storage and processing of big data sets across clusters of computers.
- Microsoft Power BI: Develop proficiency in Microsoft Power BI for business analytics and data visualization.
- Google Analytics: Learn Google Analytics for web analytics, tracking, and reporting of website traffic.
- TensorFlow: Gain expertise in TensorFlow for building and training deep learning models.
- Apache Spark: Acquire skills in Apache Spark for big data processing and analytics.
- PySpark: Learn PySpark for parallel data processing with Apache Spark using Python.
- SQL: Develop proficiency in SQL for managing and querying relational databases.
- Ansible: Acquire skills in Ansible for automating IT infrastructure tasks such as application deployment and configuration management.
- Splunk: Learn Splunk for operational intelligence, log management, and real-time data analytics.
- Pandas & NumPy: Master Pandas and NumPy libraries for data manipulation and numerical computing in Python.
- Seaborn: Develop proficiency in Seaborn for statistical data visualization in Python.
- Cassandra: Acquire skills in Cassandra for distributed database management and scalable, high-performance applications.
- Octave: Learn Octave for numerical computing and data analysis, particularly for scientific and engineering applications.
- Scikit-learn: Master Scikit-learn library for machine learning algorithms and model building in Python.
- Talend: Develop proficiency in Talend for data integration, data quality, and big data management.
- OpenCV: Acquire skills in OpenCV for computer vision and image processing tasks.
- QlikView: Learn QlikView for data visualization, business intelligence, and interactive dashboards.
Description
Delve into the realm of cutting-edge technology and data-driven decision-making with our expansive course bundle, designed to empower learners with the skills and knowledge necessary to thrive in today's rapidly evolving digital landscape. This comprehensive collection comprises over 60 specialized courses, totaling more than 300 hours of engaging content, covering a wide spectrum of topics in artificial intelligence (AI), machine learning (ML), data science, and cloud computing.
Through expert-led instruction, hands-on projects, and real-world case studies, learners will embark on a journey of exploration and discovery, delving into key concepts, methodologies, and tools essential for harnessing the power of data and emerging technologies. From mastering programming languages like Python and R to navigating advanced topics such as deep learning, predictive modeling, and cloud architecture, participants will acquire practical skills and insights to drive innovation and make informed decisions in their respective fields.
With specialized specializations and learning paths tailored to different areas of interest and career goals, learners can tailor their learning journey to suit their individual needs and aspirations. Whether aspiring data scientists, machine learning engineers, cloud architects, or business analysts, participants will find a wealth of resources and opportunities to enhance their expertise and advance their careers in the dynamic and competitive landscape of AI, ML, and data science.
Throughout the courses, learners will have the opportunity to engage with interactive exercises, quizzes, and assignments, allowing them to apply their newfound knowledge and skills in practical contexts. Additionally, mock tests and assessments provide learners with valuable feedback and insights into their progress, enabling them to track their development and identify areas for improvement.
By completing this comprehensive course bundle, participants will emerge equipped with the confidence, competence, and credentials needed to excel in their chosen field and drive meaningful impact through data-driven innovation and technology-driven solutions.
Sample Certificate
Industry Growth Trend
The overall data science platform market is expected to grow from USD 19.58 billion in 2016 to USD 101.37 billion by 2021, at a CAGR of 38.9% from 2016 to 2021. The base year considered for the study is 2015, and the forecast has been provided for the period between 2016 and 2021.[Source - MarketsandMarkets]
Average Salary
[Source - Indeed]
Requirements
- Basic understanding of programming concepts and syntax.
- Familiarity with data analysis and statistical methods.
- Knowledge of algebra and calculus for advanced machine learning and AI topics.
- Basic understanding of databases and SQL for data manipulation and querying.
- Familiarity with Linux command line for working with cloud platforms and server management.
- Understanding of fundamental networking concepts for cloud computing and IoT.
- Basic knowledge of business concepts and processes for analytics and BI courses.
- Familiarity with software development lifecycle for DevOps and software engineering courses.
- Basic understanding of data visualization principles and tools.
- Willingness to learn and adapt to new technologies and tools throughout the courses.
Target Audience
- Aspiring data scientists and AI professionals looking to master various machine learning and AI techniques.
- Software engineers interested in expanding their skill set to include data science and machine learning.
- Business analysts seeking to enhance their analytical capabilities with predictive modeling and data visualization skills.
- IT professionals aiming to transition into roles focused on big data analytics, cloud computing, and IoT.
- Students and graduates aspiring to build a career in the field of data science, artificial intelligence, or business analytics.
- Professionals from diverse industries looking to leverage data-driven insights for decision-making and strategic planning.
- Entrepreneurs and business owners seeking to understand and utilize data analytics for business growth and innovation.
- Anyone passionate about technology and eager to explore the exciting field of data science and AI.
- Individuals interested in advancing their careers by acquiring in-demand skills related to machine learning, big data, and cloud technologies.
- Organizations seeking to upskill their workforce in the latest technologies and tools to stay competitive in the digital age.
Course Ratings
Offer ends in:
Training 5 or more people?
Get your team access to 5,000+ top courses, learning paths, mock tests anytime, anywhere.
Drop an email at: [email protected]
Great course for someone like me who's been using Excel everyday but didn't know about data analysis tool available in it. I've always have to open JMP for analysis even just for simple charting and correlation plotting. Now I know I can do that in Excel too. Thanks! 🙂
Michelle Esber
This course was great to attend and learn about Excel's "Data Analytics" add-in function!
Sumari Hattingh Van Niekerk
A useful course in teaching the basics of application programming will be better if homework is activated
Ala Hussain Ali Al Turaifi
Good course on learning Stats Tools using Excel - well explained in a simple manner and it covered the basics I needed to analyse data. Very useful formulas to understand the relationship and correlation between data points in forecasting future trends. I will definitely add value to me analyzing data.
Arno Wakfer CA