Data Processing and Analysis
Skills Covered: Data Storage, Data Analysis, Machine Learning (ML), Data Preprocessing, Data Processing, Data Visualization, Relational databases, NoSQL Databases, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression, Logistic Regression, Clustering Methods, Naive Bayes, K-Nearest Neighbors, Azure, Factor analysis, Multiclass Logistic Regression, Resampling, Decision Trees, Support Vector Machines
ABOUT THIS PROFESSIONAL CERTIFICATION
The demand for skilled data analysts both in science and industry is constantly growing. Data processing and analysis Professional Certificate Program gives you the necessary knowledge base and useful skills to face data analysis challenges in your professional field.
The first course of the Program covers such concepts of data analytics as data preprocessing and visualization, large datasets management and storage by means of SQL and NoSQL database management systems, data series analysis.
The second course of the program discusses what machine learning is and mainly focuses on the regression problem (linear regression, polynomial and multivariable regression), classification methods (logistic regression, Naïve Bayes and K-nearest neighbors) and clustering methods (hierarchical and k-means clustering).
The last course covers advanced methods of machine learning. You will learn how to analyze large datasets, find regularities in your data, and apply more complicated clusterization and classification techniques. More precisely, you will face with the concept of the factor analysis under the Principal Component Analysis (PCA), learn about support vector machines (SVM) and decision trees for classification, get familiar with some popular resampling methods and apply them to the so-called Ensemble Learning. Finally, you will deal with the problem of reinforcement learning and learn some useful algorithms.
In all courses, practical tasks of each week will refine your understanding of main concepts and enhance your abilities in data engineering.
The program helps you to develop skills that include Excel data analysis, MS Azure Machine Learning Studio and Python Notebooks, Oracle Apex and Mongo DB. MS Excel and database management systems are used in the first course. Two learning tracks are provided in machine learning courses, one for those who have coding experience in Python, while the tasks in the other track are realized in MS Azure for students with no coding experience.
Founded in 1900, ITMO University is the top higher education institution in computer science in Russia, it is a trailblazer shaping national education and research policy in Russia. Higher School of Digital Culture is delighted to share with you its experience in the field of data science as well as in interdisciplinary research.
WHAT YOU WILL LEARN
- How to preprocess large datasets before the analysis: data cleaning, data smoothing and normalization methods, correct data visualization techniques and time series analysis.
- How to store and process the data by means of SQL and NoSQL database management systems.
- What are the differences between supervised and unsupervised machine learning methods and what is reinforcement learning.
- What are different classification methods (logistic regression, Naïve Bayes and K-nearest neighbors, SVM) and clustering methods (hierarchical and k-means clustering).
- How to reduce the number of variables in a dataset and how to evaluate your model.
- Data Scientists are few in number and high in demand. (source: TechRepublic)
- According to a report from job site Indeed, machine learning engineer is the best job of 2019 due to growing demand and high salaries. The career boasts a current average salary of $146,085 with a growth rate of 344 percent last year (source: Indeed)
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