Alec  Nikolaus

Alec Nikolaus


Identifying Customers Using Data

This project has been carried out as a part of the Udacity Data Scientist Nanodegree program.

Image by Markus Spiske on Unsplash

In this highly competitive world, knowing the customers well is of prime importance for a company beyond doubt to be on the top of the game. The project that we will discuss in this article revolves around this idea. The dataset used in the analysis has been provided by a German company, Arvato, which develops and implements innovative solutions for business customers around the globe. The statement of the problem is as follows :

A mail-order company, one of the clients of Arvato, sells some organic products and wants to acquire new clients. For that purpose, they are planning to conduct a marketing campaign with an aim to attract more and more people to their product. Now, the question is how can one do that efficiently?

We do not want to reach out to every individual residing in Germany (highly time-consuming and inefficient) or target people arbitrarily (risk of losing potential customers). Instead, if we can somehow separate people more likely to turn into customers from the rest, the job is done! This is where the power of data comes into the picture. The company has attributes and demographic information of their existing customers. A similar set of data for the people in Germany is also available. One can match the customer data with the general population data to identify individuals with high probabilities to become customers of the company.

The strategy

As you might already have guessed, this is essentially an unsupervised learning problem. One idea to address this problem is to use available information of both the present customers and the general population to form clusters of people characterized by a certain set of features in each case. Since the customer and general population datasets have an exactly similar structure, clusters formed from these datasets are going to be equivalent. The rest of the problem is then straightforward. One can then use these clusters to identify parts of the general population that are closer to the existing customers in the feature space. These people, therefore, are going to be the targets of the company during the marketing campaign.

Analyzing the data

In this section, we break down our entire analysis into small substeps.

1. Data preprocessing

This is the most challenging and time taking, not to mention important, part of our analysis. The datasets that we are considering here, have total 366 distinct features associated with each individual uniquely identified by an id number. The preprocessing of data involves the following steps :

  • The first thing that one should always do whenever working with a new dataset is to check for null values. It is really important to understand what features to keep and what could be dropped based on null values. Here, we drop all columns and rows exceeding a threshold of 20% on the null values.

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Figure 1: Percentage of null values in some of the features

  • By looking at the feature description file, that is provided along with the datasets, we find that some of the values in the dataset are marked as unknown.

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Figure 2: Attribute values that are marked as unknowns

We replace all these unknown values by NaNs. After this, we recheck the percentage of null values in each column as well as row which might get increased due to this replacement. If any row/column is found to exceed the threshold, we drop it.

  • If feature pairs with high correlations (>80%) are found, one of them is dropped to avoid redundancy in the information.
  • We drop categorical features with high cardinality (>15) and encode them wherever necessary.
  • If there are duplicate rows, we drop all only keeping the first occurrences.
  • We calculate z-score for each entry in the dataset and mark entries with the absolute values of z-score > 3 as outliers. If there is a significant percentage of outliers in any column/row (e.g., >10%), we drop it immediately.
  • Null values of the datasets are imputed using the strategies _most_frequent _and median (depending on the feature) of the Imputer class of the scikit-learn library.
  • Finally, we standardize all the features.

#programming #python #education #data-science #marketing

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Identifying Customers Using Data
Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Gerhard  Brink

Gerhard Brink


How Big Data Analytics Can Be Used to Improve Customer Experience?

The world today is bombarded with continuously growing big data sets. This large amount of data is being produced every minute by businesses as well as individuals. Processing such voluminous data requires advanced analytics solutions. This is where big data analytics comes in, playing an indispensable role in analysing big data sets to uncover information.

So, What is Big Data Analytics?

How Big Data Analytics Can Help Improve Customer Experience?

#big data #latest news #how big data analytics can be used to improve customer experience? #improve customer experience #big data analytics #customers

Data Lake and Data Mesh Use Cases

As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.


  • A data lake is a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the resource format and in addition to the originating data stores.
  • A data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Mesh is an abstraction layer that sits atop data sources and provides access.

According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.

Data Sources

All data sources are not equal. There are different dimensions of data:

  • Amount of data being stored
  • Importance of the data
  • Type of data
  • Type of analysis to be supported
  • Longevity of the data being stored
  • Cost of managing and processing the data

Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.


Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.

Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.

#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt