Advanced Data Science Concepts

Advanced Data Science Concepts

The “Advanced Data Science Concepts” course is designed to take your data science skills to the next level by delving into advanced topics and techniques. This course builds upon the foundational knowledge of data science and explores more sophisticated methodologies and concepts used in analyzing and interpreting complex datasets. 

Brief Program of the Course

  • Advanced Statistical Analysis
  • Machine Learning Algorithms
  • Data Visualization
  • Natural Language Processing (NLP)
  • Big Data Analytics
  • Time Series Forecasting
  • Dimensionality Reduction
  • Unsupervised Learning
  • Model Evaluation and Validation

Why Should You Learn This?

  1. Mastery of Advanced Techniques: Acquiring advanced data science concepts enables you to tackle complex data problems and extract deeper insights from diverse datasets, giving you a competitive edge in the field. 
  2. Enhanced Decision-Making: Advanced data science techniques provide you with the tools to make data-driven decisions based on sophisticated analyses and models, leading to more accurate predictions and improved business outcomes. 
  3. Career Advancement: Advanced data science skills are highly valued in the job market, with many organizations seeking professionals who can work on challenging data problems and leverage advanced methodologies. 
  4. Cutting-Edge Applications: Advanced data science concepts are at the forefront of technological advancements, driving innovations in areas such as artificial intelligence, healthcare, finance, and cybersecurity. 
  5. Research and Development Opportunities: Proficiency in advanced data science opens up opportunities to contribute to research projects, develop novel algorithms, and push the boundaries of data science. 
  6. Continued Learning: The field of data science is constantly evolving, and learning advanced concepts allows you to stay updated with the latest methodologies, algorithms, and best practices. 
  7. Solving Real-World Problems: Advanced data science skills empower you to address complex real-world challenges, such as predicting customer behavior, optimizing resource allocation, and detecting anomalies. 

In summary, the “Advanced Data Science Concepts” course provides a deep dive into advanced statistical analysis, machine learning algorithms, data visualization, and other advanced topics in data science. By mastering these concepts, you can tackle complex data problems, gain deeper insights, and make more accurate predictions, opening up a world of career opportunities and the ability to contribute to cutting-edge research and development in the field of data science. 

What Will You Learn?

  1. Advanced Statistical Analysis: Deepen your understanding of statistical concepts and learn advanced techniques for analyzing data, including hypothesis testing, regression analysis, time series analysis, and multivariate analysis. 
  2. Machine Learning Algorithms: Explore advanced machine learning algorithms, such as deep learning, ensemble methods, support vector machines, and Bayesian models. Understand their applications, strengths, and limitations. 
  3. Data Visualization: Learn advanced data visualization techniques to effectively communicate insights and patterns hidden within complex datasets. Explore interactive visualizations, geospatial mapping, and network visualizations. 
  4. Natural Language Processing (NLP): Dive into NLP techniques, including text classification, sentiment analysis, named entity recognition, and language generation. Understand how to process and analyze textual data. 
  5. Big Data Analytics: Gain insights into handling and analyzing large-scale datasets using technologies like Apache Spark, Hadoop, and distributed computing frameworks. Learn about data parallelism, data partitioning, and optimizing performance. 
  6. Time Series Forecasting: Develop skills in forecasting future trends and patterns using time series analysis techniques, such as ARIMA, SARIMA, and LSTM models. 
  7. Dimensionality Reduction: Learn advanced techniques for reducing the dimensionality of high-dimensional datasets, including principal component analysis (PCA), t-SNE, and autoencoders. 
  8. Unsupervised Learning: Explore unsupervised learning methods, such as clustering algorithms (k-means, hierarchical clustering) and anomaly detection, to discover patterns and insights in unlabeled data. 
  9. Model Evaluation and Validation: Master advanced techniques for evaluating and validating machine learning models, including cross-validation, model selection, and performance metrics specific to different domains. 


  • Course duration is 6 months (26 weeks)
  • The cost of the course is 100,000 KZT per month

Selection Criteria

  • Basic understanding of programming concepts
  • Basic knowledge of computer science
  • Pc and Web navigation skills
  • Basic knowledge of mathematical concepts (school level)

Selection is by means of Aptitude Test (online) followed by an interview (video). For enrolment, please test us via the WhatsApp button on this site, or write to