The interpretation of results is considered in every phase of the analysis. Identifying and Framing the Analytical Problem: A proper quantitative analysis starts with recognizing a problem or decision and beginning to solve it. Learn about advanced Google Analytics features including data collection, processing and configuration, and more complex analysis and marketing tools. Students will also learn to estimate the consequences of choices made for the other phases of data processing. Learn the basic features of Google Analytics including how to create an account, implement tracking code, analyze basic reports. Processing is based on the users needs and utilizing it for decision-making purposes. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods. In short, an analyst is someone who derives meaning from messy data. To implement their solutions in Python using a structured approach to programming. Data analytics is the process of collecting data in raw form. Analyzing data effectively helps organizations make business decisions. The Role of a Data Analyst A data analyst uses programming tools to mine large amounts of complex data, and find relevant information from this data.To use tools for implementing data engineering tasks (Excel in week 1, and Python with Jupiter Notebooks during the rest of the course).To choose and communicate interesting findings in the language understandable for their end user (visually or textually). Google Analytics for Beginners Learn the basic features of Google Analytics including how to create an account, implement tracking code, analyze basic reports, and set up goals and campaign.To analyze and model data (linear regression, clustering, decision tree mining, association rules learning).To choose and apply suitable visualization techniques (like line graphs, bar charts, scatter plots, pie charts, box plots, violin plots, and heat maps).To choose and apply data transformations (normalization, aggregation), data reduction, and data discretion. To clean data (missing values, duplicates, and outlier detection).To read database schemes and write simple queries to a data base in SQL. Exploratory and predictive statistics, and R for statistical analysis Basic computer programming in Python Introductory algorithms and practical machine.To use basic statistical concepts and techniques (like the mean, median, mode, percentile, range, variance, confidence intervals, p-value, correlation, and t-test).
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