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Fundamentals of working with big data (Data Science) - course RUB 14,990. from Specialist, training, Date: November 30, 2023.
Miscellaneous / / November 30, 2023
Leading teacher of the Center, head of the direction “Innovative teaching technologies”. Doctor of Technical Sciences majoring in “System analysis in information systems”. Holder of prestigious statuses PfMP(®),PgMP®,PMP®, ITIL® Expert, ITIL 4.0. Managing professional, Strategic Leader, DASA certified Product owner, accredited trainer PMP® And ITIL®, certified online training instructor PMP®,ITIL 4.0 And DASA.
She has been teaching for more than 15 years, is the author of courses and seminars at the Center, more than 80 scientific and 20 methodological works. Experience in the IT industry - over 25 years, of which more than 15 years - in the field of project management, project portfolios, products, startups; has experience in consulting on project management and organizational changes (digital transformation) in a number of large companies.
Implemented more than 20 projects in the following industries: IT (including web solutions, IT service management), education, metallurgy, insurance, telecommunications. The most famous clients with whom Danil Yuryevich worked: Siemens Telecom CIS, Microsoft, Royal Canin, PepsiCo Rus, Accenture, Pharmstandard, Myasnitsky Ryad. Danil Yuryevich has a huge
experience in building partnerships with major companies, including Microsoft, Citrix and etc.Since 2015 Danil Yurievich actively works in startups as a partner (a series of products for people with hearing impairments; online education certification system) and as a mentor (IAMCP, G-Accelerator).
Danil Yuryevich is a regular participant in international conferences, including PMXPO 2019, PMI Talent and Technology Symposium, PMI® Organizational Agility Conference and others. For two years in a row he acted as a speaker at DevOps Pro Moscow 2019-2020. Constantly improves skills at vendor trainings (DASA, Peoplecert). Successfully completed training and assessment (assessment) to become a PMP trainer according to the new version.
Using his vast experience and wonderful teaching gift, he presents the material with a large number of examples. Skillfully provokes fruitful discussions in groups and answers all questions in detail. Danil Yurievich will introduce you not to abstract methods, but to how they work in practice taking into account the legislation and peculiarities of doing business.
Special purpose teacher, holder of prestigious international status Microsoft Certified Master. Graduate of Moscow State Technical University named after N.E. Bauman.
In his classes, Fedor Anatolyevich puts the principle at the forefront “Look to the root!” - it is important not only to study the operation of the mechanism, but also to understand why it works this way and not otherwise.
A generalist in the field of software design and development. He has many years of experience as a development team leader and chief architect. Specializes in enterprise application integration, web portal architecture development, data analysis systems, deployment and support Windows infrastructure.
The combination of engineering and natural science presentation styles allows students to convey the passion and creative approach of the teacher. Fedor Anatolyevich invariably receives the most enthusiastic reviews from his grateful graduates.
Module 1. Scope of big data. Typical tasks. (1 ac. h.)
-Course objectives
-Definition of basic concepts
-History of Data Science
-Benefits from working with big data
-Typical tasks: forecast of sales, production, demand. Behavior analysis. Pattern recognition. Expert systems.
Module 2. Collection and preparation of initial data. CRISP-DM technique (1 academic. h.)
-Where to begin. Cross-industry standard methodology for working with CRISP-DM data
-Descriptive and associative study of source data
-Segmentation and data cleaning (slice and dice). Examples of Excel tools
-Data visualization in Excel. How to use pivot tables and charts
-Practical work. Segment and clean the test data set.
Module 3. Fundamentals of mathematical statistics. ANOVA. Excel add-in “Analysis package” (2 ac. h.)
-Descriptive statistics
-Average, most probable, median
-Variance, standard deviation, standard error
-Types of distributions
-Excel data analysis package
-Overview of other application tools for working with data (R, Python, Octave, MathLab, specialized databases).
-Practical work. Determine the statistical characteristics of the data sample.
Module 4. Sales forecast task. Machine learning concept. Correlation. Regression analysis (3 ac. h.)
-Statement of the problem of assessing the relationship between various factors and making a forecast
-Correlation. Pearson coefficient
-Student's test (T-analysis)
-Fundamentals of Machine Learning
-Regression analysis
-Fisher criterion
-Building and analyzing trends in Excel
-Practical work. Determine the presence of correlation and regression dependence between two data samples. Build a trend.
Module 5. Problems of classification and recognition of images, video, speech, text. The concept of neural networks. Application examples. (3 ac. h.)
-The task of segmenting discrete data using the example of recognition tasks (graphics, speech, text)
-Neural networks as a tool for solving classification problems
-Demonstration using examples of Azure, AWS
-Tasks of classifying data in social networks and finding the optimal solution (route)
-Graphs as a tool for solving problems on social graphs and predicting behavior
-Decision tree
-Partition into samples (training, testing, verification)
-Analysis of learning errors. Basis and deviations. Manual adjustment
-Practical work: classify a data set and divide it into segments.
Module 6. The challenge of social network research. The task of predicting user behavior. Social and directed graphs. Decision trees. Examples of application (3 ac. h.)
-The task of classifying data in social networks
-Graphs as a tool for solving problems on social graphs and predicting behavior
-Partition into samples (training, testing, verification)
-Analysis of learning errors. Basis and deviations. Manual adjustment
Module 7. Advanced tools: deep machine learning, artificial intelligence, fuzzy sets (1 ac. h.)
-The concept of Deep Machine Learning
-Multifactor business analysis using fuzzy logic as an example
Module 8. Career guidance for specialties in Data Science. Conclusions and recommendations for building and organizing team work (2 ac. h.)
-Roles of DS specialists: data analyst, data scientist, programmer, digital director
-Requirements for competencies and interaction of employees in the field of data analytics
-Composition and requirements for the project team for DS
-Preparing the company for the use of “bigdata”
Analytics professions: what to choose
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• We will tell you in simple words about Data Science, neural networks, artificial intelligence and other popular phenomena • You will understand what areas exist in the field of working with data, and work with analytics tools in practice • Get a step-by-step guide and find out what you need to start in the field of Data Science
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Data Analyst from scratch to Junior
You will learn to solve business problems using data. First, get the necessary training, improve your mathematics and statistics, and then study SQL, Python, Power BI and in a year you will become a data analyst.
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