Data Analysis & Statistics: practical course for beginners

Summary of the section. You will know the importance of data planning.

How to start data analysis process presented on a series of examples. You will be able to set data analysis goals and define what information and data are needed to achieve these goals.

In this assignment you will set your own data analysis goals.

Illustration of an analyst's dillema between choosing data analysis method and data collection method. You will know how to make the correct choice.

Comparison of qualitative and quantitative data. You will know the differences between the two and what they mean to your business case.

The summary of the most popular data analysis methods. You will be able to choose the correct method according to your requirements.

Presentation of 3 main ways to find your data. You will know your options in terms of data collection.

Comparison of free and paid sources of already collected data. You will know where to look for already collected data and pros and cons of this option.

Cooperation with research companies. You will know the 3 step process of ordering collection of data to the research company and whether it's worth to do that.

Why you should consider collecting data on your own in the first place. You will learn the next steps for this data collection method.

Summary of the section. You will know the importance of data exploration.

Where and who to observe. You will know how to draw conclusions from observing people from your target group.

Start talking about the topic of your data analysis project. You will know how to extract useful information from conversations.

Exploration of different text sources like websites, reports, magazines and books. You will know how to work with them for the benefit of your project.

Why scientific articles stand out from other sources of information and ideas for your project? You will know how to use scientific articles for your benefit in a fast and efficient way.

Covering TV, radio and events as sources of information. You will know the useful alternatives to the most popular sources in terms of data exploration.

You will explore data with different sources for your own project.

Review of all collected information from the exploration stage. You will know what to do with duplicated information, what are the rules in terms of variable naming and how to apply them.

You will clean up the information you have gathered and name your variables.

Connecting all information. You will know how to draw a model based on the information you have gathered.

You will create a model for your data analysis project.

Summary of the section. You will know the importance of data collection.

Different methods of respondents selection. You will know representative sample and its alternatives.

Defining the size of the group for your analysis. You will know how many respondents you should have at minimum.

You will find the right number of respondents for your business case.

10 rules to follow while creating a survey. You will know how to make your survey successful.

Review of the types of questions available for a survey. You will know how to choose the correct type of questions in different situations.

Summary of the two main types of variable measurement. You will know how to decide between reflective and formative measurement.

Comparison of nominal, ordinal, interval and ratio scales. You will know how to choose the correct measurement scale for your variable.

Creating a survey according to the rules and guidelines from the previous videos. You will know how to write a survey from scratch.

You will create a complete survey adjusted to your business case in a note app/software.

All information about testing your survey. You will know how to improve quality of your survey and avoid costly mistakes.

You will conduct test study and collect feedback for your business case.

Comparison of the most important data collection methods including in-person, telephone, mail and on-line. You will know pros, cons and application of each method.

Using Google Forms to take survey into digital. You will know Google Forms tool in depth.

You will prepare digital, interactive version of your survey.

Tips and advices to attract more respondents. You will know free and paid possibilities to gain visibility for your survey.

You will promote your survey to get all responses you need.

Summary of the section. You will know the importance of data preparation.

Closer look at the Mobile Shopping dataset. You will know what to look for in your own dataset.

The reasons of unwanted data and how to deal with them. You will know how to recognize unwanted data and what to do about it.

Causes and consequences of missing data. You will know when it is ok to have missing data and when it is not, how to find and replace missing data.

Covering five key areas of data formatting - whitespaces, typos, capitalization, data units and data types. You will know what formatting you should have to save time, effort and errors.

Why white spaces can mess up your results. You will be able to identify white spaces and remove them from your dataset using couple of different options.

How typos can affect your data presented on example, and ways to solve typos problem. You will know how to identify and correct typos with charts, filtering, find and replace and simple regular expressions.

Functions and add-ons in Google Sheets dedicated to consistent capitalization. You will know how to ensure particular capitalization for all variables, including title case, sentence case, upper case, lower case.

Explanation when unit of your data is incorrect and what are the necessary changes in such case. You will be able to decide (and implement) between conversion of data to the correct data type and removal.

Review of the available data types and when data type is wrongly assigned. You will be able to choose and assign the right data type for each variable.

Conversion of data to the required format, structure and content. You will be able to transform your data to work properly with different types of methods, software and procedures.

You will prepare your data according to all data preparation rules and guidelines.

Summary of the section. You will know the importance of data analysis.

Descriptive statistics measures presented on easy to follow example. You will know how to calculate basic descriptive statistics such as mean, median, mode, range, standard deviation and more.

Review of the essential data distributions. Basic measures and guidelines. You will be able to check whether your data is normally distributed.

Implementation of descriptive statistics learned in the theory part for the Mobile Shopping dataset. You will be able to apply the most important descriptive statistics in practice.

You will calculate descriptive statistics for your business case.

The history of inferential statistics and the presentation of the most important inferential statistics. You will know how analysis of variance, regression and structural equation modelling work.

Review of the available statistical software for PLS-SEM method with pros, cons and pricing. You will know how to choose a statistical software according to your needs and requirements.

Downloading SmartPLS 3 software from the official website. You will know how to download and install SmartPLS 3 software.

You will download Smart PLS3 software.

Walk through SmartPLS 3 software, summary of the possibilities, settings. You will know how to use all necessary options in SmartPLS 3 software.

Setting up your data analysis project. You will be able to create data analysis project and import data for this project inside SmartPLS 3.

You will create your data analysis project and import data for this project.

Review of all required steps for the creation of the groups including settings. You will know how to create different groups of respondents inside SmartPLS 3.

You will create different groups of respondents for your business case.

Create a digital version of a model for your data analysis, adjust the look and feel. You will know how to correctly create a model and include all relationships between variables inside SmartPLS 3 software.

You will create a model for your business case.

Summary of methods and procedures with explanation how they work. You will know what methods and procedures to use in SmartPLS 3 to receive required results and to achieve your goals.

Definition of reliability and importance of establishing reliability of your results. You will be able to check reliability of your results inside SmartPLS 3 software.

You will check whether your results are consistent and stable.

Definition of validity and importance of establishing validity of your results. You will be able to check validity of your results inside SmartPLS 3 software.

You will check whether your results are true and accurate.

Covering model fit concept and its metrics. You will know how to check the goodness of fit measure.

Things you should consider while generating results for your data analysis project. You will be able to check the most important results including: path coefficients, statistical significance and R square value.

Summary of ways to check differences between groups for different data analysis methods. You will be able to check differences in behavior for different groups of respondents.

Explanation of mediation concept on two examples. You will be able to check mediation reationships in your data analysis project.

Presentation of different export options. You will be able to export your data to different formats in SmartPLS 3.

Practical conclusions for each relationship inside your model. What your results tell about consumers, what do they mean for your company and business in general. You will be able to interpret your results and prepare valuable business recommendations.

You will propose business recommendations based on your main results.

Providing more precise suggestions to business by looking at the results of different groups of respondents separately and covering the potential mediation effect. You will be able to give different recommendations from your results for different groups of people and know what type of mediation occured and how it affects your conclusions.

You will propose business recommendations based on the results of your group analysis.

Limitiations of your data analysis project, things that were not covered. You will be able to plan your future data analysis project and improve its quality.

Summary of the section. You will know the importance of data monetization.

Top ten best practices and guidelines on how to visualize your data. You will be able to tell the story through data and transform data to a good looking insights.

Comparison of pros, cons and usage of the most useful charts. You will have no problem with choosing the right chart type for each situation.

Visualization of data in Google Slides. You will be able to use Google Slides to make your data look more attractive to the user.

You will prepare the visualization of your results to make them more accessible and interesting.

Turning recommendations from data analysis into practical implementation. You will know how to prepare to the implementation meeting including dealing with the attitudes of your coworkers.

Comparison of generating money from your data analysis report vs selling data. You will know how to make revenue from your data analysis project.

Presentation of the main marketing activities that can be done after your data analysis is finished. You will know how to use your data analysis report to ensure it has the full exposure in front of people who are important for your business.

Summary of the course. You will see what you have learned throughout the course and where to go from here.

Summary of the section. You will know what type of content will be added in the future.

Formative measurement presented on the Corporate Reputation Project example. You will be able to measure variable in a formative way with required measures.

F square as a supplement or alternative to the traditional approach of the variable measurement. You will be able to interpret f square value for your data analysis project.