Aleksandr Kosenko - Aleksandr Kosenko Digital (Page 2)

5 – Channels

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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6 – Revenue Streams

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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7 – Key Activities

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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8 – Key Resources

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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9 – Key Partners

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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Product Management

10 – Cost Structure

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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Product Management

11 – Eco-Social Costs

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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Product Management

12 – Eco-Social Benefits

❮ ❮ ❮ back forward ❯ ❯ ❯ A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer

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Product Management

13 – KPI (Key Performance Indicators)

❮ ❮ ❮ back A look at the business model from the product manager's point of view CANVAS 13 - Great guide on the business model, from the product manager's point of view 1 – Customer Problem 2 – Customer Segments 3 – Value Propositions Value Proposition Formulation Map 4 – Customer Relationships

Data Analitics Study Notes

Visualization considerations

Guidelines and pro tips Refer to the table below for recommended guidelines and style checks for headlines, subtitles, labels, and annotations in your data visualizations. Think of these guidelines as guardrails to prevent your visualizations from becoming too crowded or busy. This can confuse or distract the audience with unnecessary

Data Analitics Study Notes

Data Visualisation

Introduction to Data Visualization and Design Principles

Data Analitics Study Notes

Types of data validation

© Google This text outlines the purpose, examples, and limitations of six data validation types. The first five are linked to the data itself: type, range, constraint, consistency, and structure. The sixth type pertains to application code validation that accepts user input. As a junior data analyst, you may not conduct

Data Analitics Study Notes

Get started with data calculations

* As a data analyst, familiar tools and processes are frequently used, but learning new ones is essential for growth. * SQL can significantly improve efficiency when pulling data and conducting analysis. * Spreadsheets, conditional formulas, the SUMPRODUCT function, and pivot tables are useful tools for performing calculations in data analysis. * Pivot tables

Data Analitics Study Notes

Use subqueries to aggregate data {SQL}

WarehouseWarehouse.csv484 Bytesdownload-circleOrdersOrders.csv352 KBdownload-circle HAVING function Allows you to add a filter to your query instead of the underlying table that can only be used with aggregate functions CASE function Returns records with your conditions by allowing you to include if/then statements in your query Aliasing When you

Data Analitics Study Notes

Use subqueries to refine data {SQL}

Open BigQuery and create a free account there, upload bigquery-public-data and select new_york_citibike. Scenario Review the following scenario. Then complete the step-by-step instructions. You work for an organization that is responsible for the safety, efficiency, and maintenance of transportation systems in your city. You have been asked to

Data Analitics Study Notes

Explore how SQL JOINs work

Common JOINs * INNER JOIN: a function that returns records with matching values in both tables * LEFT JOIN: a function that returns all the records from the left table (first mentioned) and only the matching records from the right table (second mentioned) * RIGHT JOIN: a function that returns all records from

Data Analitics Study Notes

Aggregate data for analysis

Data aggregation The process of gathering data from multiple sources in order to combine it into a single summarized collection * Puzzle pieces = data * Organization = aggregation * Pile of pieces = summary * Putting the pieces together = gaining insights The aggregation process helps to identify trends, make comparisons, and gain insights that wouldn'

Data Analitics Study Notes

VLOOKUP {data aggregation}

VLOOKUP core concepts Spreadsheet functions can be used to quickly find information and perform calculations using specific values. VLOOKUP, or Vertical Lookup, is one such function that vertically searches for a certain value in a column to return a corresponding piece of information. In this reading, you’ll examine the

Data Analitics Study Notes

Exploratory data analysis or EDA

Review * Practices of EDA * PACE workflow * Ethics of working with data * Data visualizations Exploratory data analysis (EDA) The process of investigating, organizing, and analyzing datasets and summarizing their main characteristics, often employing data wrangling and visualization methods. EDA is an important first step in understanding any new dataset or question.

Portfolio process

Portfolio module 2 {Get Started with Python} Google Advanced Data Analytics Certificate

At the beginning there are sc emails from managers with a request. Then my preparation for the PACE strategy document. Then questions from the laboratory and my answers to them. Then the lines of code that were necessary to get answers to the questions. And at the end, a summary

Data Analitics Study Notes

Format and adjust data

Check and change data type Check your data for inconsistent units of measurement to prevent problems during data analysis. Convert temperatures from Fahrenheit to Celsius Use the CONVERT function to change units of measurement. Convert function formula as =CONVERT(CELL, from Fahrenheit to Celsius. Enter "F", "C&

Data Analitics Study Notes

Organize data for analysis

The analysis process Analysis – The process used to make sense of the data collected The goal of analysis is to identify trends and relationships within data so you can accurately answer the question you're asking The 4 phases of analysis 1. Organize data 2. Format and adjust data

Data Analitics Study Notes

Document the cleaning process

Documentation The process of tracking changes, additions, deletions, and errors involved in your data-cleaning effort * Recover data-cleaning errors * Inform other users of changes * Determine quality of data A changelog, a file containing a chronologically ordered list of modifications made to a project, is typically utilized to track these changes. 1.

Data Analitics Study Notes

Manually cleaning data

Verify and report results 1. Verification of data is crucial to ensure the data cleaning process has been executed correctly and the data is accurate and reliable. 2. Verification allows for the identification and rectification of errors before beginning analysis, ensuring the insights gained can be trusted for decision-making. 3.

The transaction game: How to increase your sales and retain customers

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Product Management

The transaction game: How to increase your sales and retain customers

The companies that operate mainly offline focus on increasing the number of transactions, where the exchange of value occurs between the business and the customer. For a product manager, this type of service can be called a "transaction game." Typical representatives of these companies are Mercadona, Amazon, Shein,