Data Analyst specializing in BPM, Sales, and Digital Marketing to Uncover Powerful Insights from Your Data
I have bachelor's degree in Economics and Management, 10 years of experience in managerial, sales and digital marketer roles. This gives me a solid foundation to analyze data, predict trends, and make effective decisions.
In my spare time I love work on my own project Thor. Feel free to check it out in my portfolio below.
Over the past few years I've developed an extensive skill set to prepare for a future career in the tech industry.
Let me help make better use of Your data. My expertise can be used to clean, analyse, interpret and visualize data.
Before starting the analysis, the collected data must be prepared. One of the stages of preparation is data cleaning using special algorithms, techniques and tools.
Remember, the overall goal of data cleaning is to increase the data’s integrity (i.e., its reliability, accuracy and consistency).
What is “Dirty” Data?
Incomplete: the absence of certain attributes or their values of interest, or containing only aggregated data.
Noisy: Values contain errors or outliers.
Inconsistent: contains inconsistencies in codes or names.
Before gathering or analyzing any data, you first need to determine whether a relationship exists between two variables; say, blood pressure and a new drug, “drug x.”
Data duplication. The same object appears in the table twice. For example, it comes from two different sources or was duplicated due to an erroneous write operation. This breaks the statistic as it doubles the value that should be single.
Non-unique values. Sometimes a unique parameter must correspond to a particular entry. For example, two different people cannot have the same insurance policy number. If the number is the same in two records, this is an error. As a result, the processing of such data may fail and not complete.
Data inconsistency. In this case, there is only one object, there are several records related to it, and the data in these records is not the same. For example, given different phone numbers. This error occurs when collecting data from multiple sources and needs to be corrected - merged or deleted. Otherwise, the algorithm will either perceive the objects as different, or it will not be able to process them correctly as one.
Missing values. There is simply no value in the table, for example, a phone number or address of a client - it was either not entered or lost during the transfer.
Invalid date. The value is there, but wrong. For example, a Russian phone number that starts with the number 9. This error occurs if you do not configure filters on data collection.
Spelling errors and typos. The word is entered incorrectly, it distorts the statistics and does not allow filtering the values.
Anomalous values. For example, a certain product is often sold in the amount of 10 pieces per day, and suddenly, on the most ordinary day, sales sharply exceeded 1000. Such an unexpected surge is most likely the result of an error, and it can be considered a missing value, because the real one was not recorded. To resolve the error, a data cleansing operation is required.
Yes, data preparation is not an easy task and can sometimes even be painful. But the effort invested at this stage will pay off a hundredfold when the model runs. It is not uncommon for even simple models to show excellent results quite quickly and require minimal calibration if they work on well-prepared data.
In data analytics, exploratory data analysis is how we describe the practice of investigating a dataset and summarizing its main features. It is a form of descriptive analytics. EDA aims to spot patterns and trends, to identify anomalies, and to test early hypotheses. Although exploratory data analysis can be carried out at various stages of the data analytics process, it is usually conducted before a firm hypothesis or end goal is defined.
In general, EDA focuses on understanding the characteristics of a dataset before deciding what we want to do with that dataset.
Specifically, some key benefits of an EDA include:
Spotting missing and incorrect data.
Understanding the underlying structure of the data.
Testing hypothesis and checking assumptions.
Calculating the most important variablese.
Determining error margins.
Identifying the most appropriate statistical tools to help.
Exploratory data analytics often uses visual techniques, such as graphs, plots, and other visualizations. This is because our natural pattern-detecting abilities make it much easier to spot trends and anomalies when they’re represented visually. As a simple example, outliers (or data points that skew a trend) stand out much more immediately on a scatter graph than they do in columns on a spreadsheet.
Utilizing tools like Tableau or PowerPoint to create crisp, compelling dashboards, and visualizations. Tableau is a leader in the field of business intelligence and an industry-standard tool for data visualization.
Simply put, data visualization is the representation of information in a pictorial format such as charts or diagrams. You may have heard the phrase “a picture’s worth a thousand words” before. Let’s consider the meaning behind it.
Frequencies & Distributions. Histograms are considered statistical visualizations for displaying the distribution of data within a data set.
Box Plots. Box plots provide a way to visualize descriptive statistics, which means that they’re best suited for quantitative data. They allow you to see the median and data quartiles for a given variable.
Composition Charts focus on how the different parts of a data set compare to the whole (appropriately, they’re sometimes referred to as comparison charts). You can think of them as indicators of proportion—for example, what proportion (or percentage) of a population plays an instrument? Of those who play an instrument, which instrument do they play?
Temporal Charts are those that include some sort of time component. In most analyses, time as a variable requires special consideration. The most common temporal chart is the line chart. By their very nature, line charts display data over multiple time periods, with the most typical format indicating time along the horizontal (or x) axis.
Statistical Charts are similar to composition charts in that there are many different types of visualizations that fall under this category. They do, however, share one common purpose: to display some statistical aspect of the data.
Geospatial Charts. Maps are used for more than just navigation and are an excellent way to perform spatial analysis (i.e., comparing something across regions). For this purpose, there are choropleth maps, dot (or distribution) maps, and heat maps, each of which can be used for different purposes.
Textual Analysis is the only category that focuses on qualitative analysis. Qualitative data is usually non-numeric and relies on the senses: what you see, smell, taste, hear, or feel.
There are so many ways in which data visualization creates new opportunities for communication. It can highlight patterns or trends, act as a mechanism for sharing insights, and even engender new perspectives on known issues.
To begin with, this was the most difficult topic to study. However, with deep study and practice, I am sure that I can achieve practical results. On student projects with CareerFoundry, I practiced in projects with supervised, unsupervised and analyzing time series data.
What I did during the study:
Split the data into training set and test set.
Applied regression algorithms (linear regression in Python) to the training set, allowing the algorithm to learn from this data.?
The result of this learning process was a predictive model.
Then I fit the model into the test set.
Finding out whether the model is accurate enough for future use.
The elbow technique. Determined the range of potential clusters in the data.
Plotted the elbow curve using PyLab.
Interpreted the logic of clusters on the chart.
K-means clustering. Created the k-means object and Fited the k-means object to the data.
Plotted the clusters.
Source time-series data relevant to project data via an API.
Setting Up Jupyter for a Time Series Analysis.
Subsetting, Wrangling, and Cleaning Time Series Data.
Visualize the data in a line plot and decompose its structure.
Conductind a Dickey-Fuller test and ploting autocorrelations to test for stationarity.
Performing differenc to stationarize non-stationary data
”Frankly, this process is quite basic: find the pattern, apply the pattern.”
What people say about me
“Halina has always had outstanding analytical skills and impressive adaptability and efficiency. Her work was critical to important results. Therefore, I highly recommend her for any work related to data analysis or project management.”
“I studied with Halina at one of the project management courses. I can say that her final project showed strong results in terms of analysis, presentation of the future forecast and visual design for a clear understanding of all aspects.”
“I worked with Halina, where she was the head of the sales department. She was very efficient and responsive to requests from specialists, and was independent in finding the best solutions to the problems that the business presented to her.”
Where I express personal thoughts in words
Data enrichment, data preparation, data cleaning, data scrubbing—these are all different names for the same thing: the process of fixing or removing incorrect, corrupt, or weirdly formatted data within a dataset.
Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted.
But, as we mentioned above, it isn’t as simple as organizing some rows or erasing information to make space for new data.
Data cleaning is a lot of muscle work. There’s a reason data cleaning is the most important step if you want to create a data-culture, let alone make airtight predictions. It involves:
Streets empty that are usually busy are remarkable and can evoke the sense of historical pictures from before the invention of the motorcar. Other things that are different at the moment will be queues to get into stores and the lines marked out on the floor to show how far apart we should be.
It’s said that the majority of a data scientist's time is spent on cleaning, rather than machine learning. In fact, 45% of data scientist's time is spent on preparing data. And to us, that makes sense—if there’s data that doesn’t belong in your dataset, you aren’t going to get accurate results.
And with so much data these days, usually combined from multiple sources, and so many critical business decisions to make, you want to be extra sure that your data is clean.
Businesses have a plethora of data. But not all of it is accurate or organized. When it comes to machine learning, if data is not cleaned thoroughly, the accuracy of your model stands on shaky grounds. We've talked about how no-code simplifies the traditional machine learning process. What is typically a 10-step process instantly becomes a much simpler route with platforms like Obviously AI.
But the traditional and no-code process still require the same important first step: connecting to your data. And all that time you've saved by opting for a no-code tool won't matter if those large stacks of data aren't properly organized, formatted, or accurate.
Preparing your data helps you maintain quality and makes for more accurate analytics, which increases effective, intelligent decision-making.
These are the kinds of benefits you’ll see:
The short answer is no. Unless you are trained in data science, or already know how to prepare your dataset, we typically advise against jumping right in.
The best way to explain this is with an analogy.
Most people know how to drive a car. But not everyone knows how to drive a race car. Driving a regular car and driving a race car are two very different things. But because they have the same fundamentals (press gas, brake, turn wheel), there are those who think that they’ll be able to successfully drive a race car at the racetrack.
Race cars, however, put out raw power and it is largely up to the driver to filter that force as the car is driven. So, while you could absolutely go out on that racetrack and drive that race car, chances are, you won’t be able to drive it well or get the most out of your experience. You might even crash.
The same thing can be said about data. Everyone knows how to operate an excel spreadsheet. But oftentimes, the dataset in that spreadsheet isn’t set up for building machine learning models.
Let’s say you’re trying to predict housing prices. You have a lot of data on sellers: their demographics, the amount they sold their house for, etc. You might also have data that appears to be irrelevant to what you want to predict. But that outlier may be crucial to your predictions. And machine learning will catch that.
This is why we always advise meeting with our team first before getting started on your first predictions and what we mean when we say that data cleaning is more than just formatting spreadsheets. And typically, we find that most people that go through onboarding have the most success when they see how data needs to be prepped.
Once you know what to look out for, it’s easier to know what to look for when prepping your data. While the techniques used for data cleaning may vary depending on the type of data you’re working with, the steps to prepare your data are fairly consistent.
Here are some steps you can take to properly prepare your data.
Duplicate data most often occurs during the data collection process. This typically happens when you combine data from multiple places, or receive data from clients or multiple departments. You want to remove any instances where duplicate data exists.
You also want to remove any irrelevant observations from your dataset. This is where you data doesn’t fit into the specific problem you’re trying to analyze. This will help you make your analysis more efficient.
Outliers are unusual values in your dataset. They’re significantly different from other data point and can distort your analysis and violate assumptions. Removing them is a subjective practice and depends on what you’re trying to analyze. Generally speaking, removing unwanted outliers will help improve the performance of the data you’re working with.
Remember: just because an outlier exists, doesn’t mean it is incorrect. Sometimes an outlier will help, for instance, prove a theory you’re working on. If that’s the case, keep the outlier.
Structural errors are things like strange naming conventions, typos, or incorrect capitalization. Anything that is inconsistent will create mislabeled categories.
A good example of this is when you have both “N/A” and “Not Applicable.” Both are going to appear in separate categories, but they should both be analyzed as the same category.
Make sure that any data that’s missing is filled in. A lot of algorithms won’t accept missing values. You may either drop the observations that have missing values, or you may input the missing value based on other observations.
Once you’ve thoroughly prepped your data, you should be able to answer these questions to validate it:
Data Prep Checklist: The Basics.
Obviously AI requires a structured dataset to get meaningful prediction outcomes. We made a quick DIY checklist to ensure your data is well structured and machine learning ready. It was prepared by the data science team at Obviously AI, so you know it’s comprehensive.
What columns should I bring in my dataset?.
A training dataset that's machine learning ready typically contains several types of columns (features), while you don't need them all, having as many as possible can help make better predictions.
Here's a list of most common column types:
Data cleaning is an extremely vital step for any business that is data-centric. Businesses that take proper care of their datasets are rewarded with high-quality predictions and are able to make leaps ahead of their competition. With clean and organized data, you can predict anything—from customer churn to hospital stay to employee attrition.
Obviously AI has a team of data scientists that become an extension of your team helping you make your datasets machine learning-ready. Book a demo with us today to learn more about how our dedicated data scientists team can help you get your data machine-learning ready.
Companies and businesses today need modern programming tools in order to build the many, many advanced tools and solutions they need to keep their operations running seamlessly.
So, what do companies use to build business analysis solutions? Python! Why? Because it is easy to learn, offers high-quality community support, etc.
Python is an all-purpose programming language. It is used for creating different types of programs, and this implies that it is not specialized for a range of specific problems. Did you know – it is the first choice for development teams because of its OOPS concept. It allows them to concentrate on Rapid Action Development (RAD) and offers an easy-to-use interface. So, whether you are looking to develop software, web application, or even complex enterprise software—Python has you covered.
To better understand why Python is so good for business analytics, let us take a closer look at the solutions it can help companies build:
1.Descriptive analytics: Descriptive analytics, usually performed by data analytics, is meant to help companies gain better insight into what has happened as well as analyze historical market trends. Python-based data analysis tools, then, allow companies to profiling their data, visualize results, and identify precise opinions that help drive better decisions and next steps for the company.
2.Predictive analytics: Python can also be used to create custom machine learning tools and solutions to enable companies to run predictive analytics for their business. What is predictive analytics? It is the analysis of data to enable companies to brace for future market conditions by offering insights into possible events. Python is also great for machine learning solutions and networks for decision trees, Bayesian networks, etc.
3.Prescriptive analytics: Prescriptive analytics, a.k.a. decision science, is the last step of business analytics. The goal of prescriptive analytics is to help companies to foretell specific outcomes as well as the what, when, and why of these outcomes. Python-based development, then, enables creation of solutions to build prescriptive analytics tools such as deep learning, which makes use of artificial neural networks to deliver better outcomes.
4.Advanced modeling: There are countless ways for businesses to use their data and one of them is advanced data modeling driven by artificial intelligence and machine learning to access high-value insights. In this regard, Python helps by making it easier for developers to build apps for product classification analysis, price forecasting, economic modeling, market segmentation study, product price elasticity estimation, etc.
5.Working with big data: Most companies in this day and age are looking for newer and modern ways to improve operations and better serve their customers. And in order to do that, they need tools to help them glean insights from all their data. This is where Python comes in handy, allowing companies to develop custom big data analysis apps to access the insights they seek.
6.Automation: Yet another reason why Python-based app development for business analysis is so highly recommended is automation. You see, Python enables the development of apps that can empower business operations with the ability to automate and replicate data and processes such as error handling in text data to merge different datasets, repetitive analysis of markets, etc.
There is simply no denying that Python is among the most popular programming languages in the world and it is so for a reason. Actually, it is considered to be a rock-solid programming language for a variety of reasons, including the fact that it is conducive to business analytics. The aforementioned uses happen because Python makes it easier for businesses to work with big data, implement automation, use advanced modeling, and so much more.
Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.
If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.
To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.
A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”
Hypothesis testing, then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.
When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data, or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.
The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.
As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.
In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.
1. Alternative Hypothesis and Null Hypothesis
In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis. Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis, on the other hand, is a statement that’s meant to show there’s no statistical relationship between variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.For example, consider a company’s leadership team who historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.
In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”
The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.
2. Significance Level and P-Value
Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.
With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.
In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.
3. One-Sided vs. Two-Sided Testing
When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests, or one-tailed and two-tailed tests, respectively.
Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.
4. Sampling
To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.
A survey involves asking a series of questions to a random population sample and recording self-reported responses.
Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.
Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.
Countless factors impact every facet of business. How can you consider those factors and know their true impact?
Imagine you seek to understand the factors that influence people’s decision to buy your company’s product. They range from customers’ physical locations to satisfaction levels among sales representatives to your competitors' Black Friday sales.
Understanding the relationships between each factor and product sales can enable you to pinpoint areas for improvement, helping you drive more sales.
To learn how each factor influences sales, you need to use a statistical analysis method called regression analysis.
If you aren’t a business or data analyst, you may not run regressions yourself, but knowing how analysis works can provide important insight into which factors impact product sales and, thus, which are worth improving.
Before diving into regression analysis, you need to build foundational knowledge of statistical concepts and relationships.
One of the cardinal rules of statistically exploring relationships is to never assume correlation implies causation. In other words, just because two variables move in the same direction doesn’t mean one caused the other to occur.
If two or more variables are correlated, their directional movements are related. If two variables are positively correlated, it means that as one goes up or down, so does the other. Alternatively, if two variables are negatively correlated, one goes up while the other goes down.
A correlation’s strength can be quantified by calculating the correlation coefficient, sometimes represented by r. The correlation coefficient falls between negative one and positive one.
r = -1 indicates a perfect negative correlation.
r = 1 indicates a perfect positive correlation.
r = 0 indicates no correlation.
Causation means that one variable caused the other to occur. Proving a causal relationship between variables requires a true experiment with a control group (which doesn’t receive the independent variable) and an experimental group (which receives the independent variable).
While regression analysis provides insights into relationships between variables, it doesn’t prove causation. It can be tempting to assume that one variable caused the other—especially if you want it to be true—which is why you need to keep this in mind any time you run regressions or analyze relationships between variables.
With the basics under your belt, here’s a deeper explanation of regression analysis so you can leverage it to drive strategic planning and decision-making.
Regression analysis is the statistical method used to determine the structure of a relationship between two variables (single linear regression) or three or more variables (multiple regression).
One way to think of regression is by visualizing a scatter plot of your data with the independent variable on the X-axis and the dependent variable on the Y-axis. The regression line is the line that best fits the scatter plot data. The regression equation represents the line’s slope and the relationship between the two variables, along with an estimation of error.
Physically creating this scatter plot can be a natural starting point for parsing out the relationships between variables.
There are two types of regression analysis: single variable linear regression and multiple regression. Single variable linear regression is used to determine the relationship between two variables: the independent and dependent. The equation for a single variable linear regression looks like this:
In the equation:
ŷ is the expected value of Y (the dependent variable) for a given value of X (the independent variable).
x is the independent variable.
α is the Y-intercept, the point at which the regression line intersects with the vertical axis.
β is the slope of the regression line, or the average change in the dependent variable as the independent variable increases by one.
ε is the error term, equal to Y – ŷ, or the difference between the actual value of the dependent variable and its expected value.
Multiple regression, on the other hand, is used to determine the relationship between three or more variables: the dependent variable and at least two independent variables. The multiple regression equation looks complex but is similar to the single variable linear regression equation:
Each component of this equation represents the same thing as in the previous equation, with the addition of the subscript k, which is the total number of independent variables being examined. For each independent variable you include in the regression, multiply the slope of the regression line by the value of the independent variable, and add it to the rest of the equation.
You can use a host of statistical programs—such as Microsoft Excel, SPSS, and STATA—to run both single variable linear and multiple regressions. If you’re interested in hands-on practice with this skill, Business Analytics teaches learners how to create scatter plots and run regressions in Microsoft Excel, as well as make sense of the output and use it to drive business decisions.
You can use a host of statistical programs—such as Microsoft Excel, SPSS, and STATA—to run both single variable linear and multiple regressions. If you’re interested in hands-on practice with this skill, Business Analytics teaches learners how to create scatter plots and run regressions in Microsoft Excel, as well as make sense of the output and use it to drive business decisions.
It’s important to note: This overview of regression analysis is introductory and doesn’t delve into calculations of confidence level, significance, variance, and error. When working in a statistical program, these calculations may be provided or require that you implement a function. When conducting regression analysis, these metrics are important for gauging how significant your results are and how much importance to place on them.
Once you’ve generated a regression equation for a set of variables, you effectively have a roadmap for the relationship between your independent and dependent variables. If you input a specific X value into the equation, you can see the expected Y value.
This can be critical for predicting the outcome of potential changes, allowing you to ask, “What would happen if this factor changed by a specific amount?”
Returning to the earlier example, running a regression analysis could allow you to find the equation representing the relationship between employee satisfaction and product sales. You could input a higher level of employee satisfaction and see how sales might change accordingly. This information could lead to improved working conditions for employees, backed by data that shows the tie between high employee satisfaction and sales.
Whether predicting future outcomes, determining areas for improvement, or identifying relationships between seemingly unconnected variables, understanding regression analysis can enable you to craft data-driven strategies
In the modern gaming industry, it is impossible to create a successful mobile or social project without processing large amounts of information. To design and support products, write marketing strategies and monetize, a lot of game analytics tools are used: within one project there can be several of them at once, it all depends on the goal.
The tasks of an analyst include writing standards for collecting data (how, when, in what format the system will collect them from players), and thinking through ways to process them, and predicting possible problems. In addition, on the basis of the data obtained, it is also necessary to justify the changes in the project - how it is possible to eliminate difficulties, why, with what numbers it can be confirmed and how to check that the changes have improved the situation.
Usability, output transparency, input ease of processing, credibility, consistency with partner data are secondary goals, but they take no less time than the main ones.
Analytics allows you to quickly find the reasons that force users to leave the game, identify the risk group and, based on the information recorded in daily reports, develop strategies to retain players. Product optimization uses mobile and social gaming analytics applications provided by Claritics, Kontagent, Mixpanel, Flurry, Totango and Google Analytics. However, in addition to external systems, analysts can also use internal programs designed to solve specific problems for processing both in-game events and incoming traffic.
Each specific problem needs its own set of data inside and outside the game, and it is desirable to collect them as early as possible, ideally, anticipating likely bottlenecks. For example, you can establish the dependence of each traffic channel on the user's life path, breaking this path into stages: from the beginning of installation to full pumping and payments. A chain of custom events not only creates a convenient funnel for each traffic provider (and allows you to count the conversion), but also makes it possible to determine the exact place of loss at each stage. If any stage shows the failure of the players, we can detail it before establishing the reasons.
The choice of analytics systems and indicators depends on which department of the company - producers, marketers, QA or game designers - the report will be prepared for, what hypotheses need to be formulated and tested. Often the same data is processed in different systems, duplicated by different indicators and included in several reports for different departments - for example, error messages and performance indicators are critical for testers, but they will only interfere with the producer.
To select or build an effective analytical system, the following sequence of questions should be followed:
The tasks of collecting and processing data in gaming companies are usually assigned to the analytics of online games. At various stages of development, these tasks will change: from the initial collection and testing of the work of analytical systems to the transformation of the received data into specific recommendations and proposals for other departments.
For the purpose of demonstrating A/B testing with Python, let's consider a hypothetical dataset that represents user engagement with two different versions of a website feature. This dataset will include user IDs, the version of the feature they were exposed to (A or B), and whether they engaged with the feature (clicked a button, made a purchase, etc.) represented as a binary outcome (0 = no engagement, 1 = engagement).
This code snippet creates a dataset with 100 entries, 50 for each version (A and B), with a simple pattern of engagement for demonstration purposes. In a real-world scenario, the engagement data would result from the actual interactions of users with the feature variants.
Once you run this code, you can download the generated CSV file and use it for A/B testing analysis with Python as described in the article. Let's proceed to generate this dataset.
The example dataset for A/B testing has been created and saved. You can download it using the following link:
https://drive.google.com/file/d/1qSgS8XnXJbVdqkOFd5KN_vg7MSbkyiax/view?usp=drive_link
This CSV file contains user IDs, the version of the feature they were exposed to (A or B), and their engagement as a binary outcome. You can use this dataset to practice A/B testing analysis with Python, as outlined in the article.
A tech company, "Tech Innovate," is experimenting with two different call-to-action (CTA) buttons on its product landing page. Version A has a blue CTA button saying "Sign Up for Free," and Version B has a green CTA button with the same text. The goal is to determine which color leads to a higher conversion rate, defined as the percentage of visitors who sign up.
A tech company, "Tech Innovate," is experimenting with two different call-to-action (CTA) buttons on its product landing page. Version A has a blue CTA button saying "Sign Up for Free," and Version B has a green CTA button with the same text. The goal is to determine which color leads to a higher conversion rate, defined as the percentage of visitors who sign up.
Assume we've collected data from 1,000 visitors, with 500 exposed to Version A and 500 to Version B. The outcome of whether a visitor signed up (1) or not (0) is recorded. This data is simulated and stored in two Pandas DataFrames for simplicity.
Before conducting the statistical test, it's helpful to visualize the conversion rates of the two versions.
This bar chart will show the conversion rates for Versions A and B, providing a visual comparison.
To determine if the difference in conversion rates is statistically significant, we can perform a two-sample t-test.
A p-value less than 0.05 typically indicates that the difference in conversion rates is statistically significant, allowing us to conclude which version performs better.
If the p-value is below 0.05, we can reject the null hypothesis (no difference in conversion rates) and conclude there is a statistically significant difference between the two versions. For instance, if Version B (green CTA button) has a significantly higher conversion rate, Tech Innovate might decide to implement this change across their site.
This example illustrates the process of conducting an A/B test using Python, from data preparation and visualization to statistical testing and result interpretation. By applying these principles, data scientists can effectively use A/B testing to make data-driven decisions and optimize user experiences.
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