Online courses, with expert mentors.

Our courses are totally online, but they are not like the online courses you have seen. You will not be watching boring videos and taking tests; You will learn by doing, with the help of expert mentors who are always available to provide meaningful advice and feedback on your work.

The Data Analytics / Big Data Certificate program is designed by universities: Carnegie Mellon, Northwsetern and Yale.

The program is designed for people who focus on making decisions based on data. The student will learn how to analyze structured and unstructured data, interpret those results to extract value, communicate them to decision makers and other non-technical audiences.

Students will learn these techniques within a value framework and acting in roles, presenting real-life project projects with an experienced mentor as a guide. Mentors do not teach, but help students learn and develop skills that are relevant to the work they are doing. Mentors provide in-depth comments on student projects and make recommendations to improve the process and encourage additional student growth.

Students will use powerful data analytics tools while perfecting soft skills such as identifying the types of problems that data analytics can solve and that are effectively presented to interested parties. All the material is in English and the sessions and reports will be done in Spanish.

Additionally, you will learn and practice the cognitive skills essential to success in all areas of Data Analytics / Big Data. These include:

  • Mastery of a wide range of skills to work.
  • Comprehensive project experience in real world problems.
  • A portfolio of professional quality jobs.
  • A certificate of completion.


  • Data analysis: understanding the clients. (6 ECTS)
  • Data analysis: prediction of profitability and customer preferences. (6 ECTS)
  • Deep analytics and visualization. (6 ECTS)
  • Big Data: web mining. (6 ECTS)

Note: 1 ECT is equal to 25 hours

Course 1: Data analysis: understanding the clients.

What will he do in the course

  • It will use data mining tools to investigate patterns in complex data sets.
  • Pre will process data for data mining, for example: it will transform numerical values to nominal values, it will decrypt data, it will handle non-existent data).
  • Understand and identify parametric and non-parametric data.
  • It will use decision tree algorithms to answer questions that involve nominal data.
  • It will use regression algorithms to investigate problems involving continuous numerical data.
  • It will display data and identify types of data distributions.
  • It will apply cross validation and create predictive models.
  • Interpret and outline inferences about the results of data mining.
  • It will assess the predictive performance of machine learning models by means of key error metrics.
  • It will identify when the models fail or obtain points of view with the whys within an error analysis.
  • It will outline relationships between performance and the measured characteristics of an automatic learning model to help understand the performance of a model.
  • Will investigate and address problems related to collinearity and adjustment.
  • Identify and understand the reduction of dimensions.
  • Prepare and present the results of data mining to interested parties without a technical profile.

Course 2: Data Analytics: Predicting customer preferences.

What will he do in the course

  • It will explore patterns in the data to create models to predict new ones, for example: predict preference for a brand for online customers).
  • It will run similarity analyzes to recommend products using association rules.
  • It will create SQL queries to extract data from an existing database.
  • It will deepen the experience with decision trees to predict preferences for a brand.
  • It will use classifiers such as Nearest Neighbor and Support Vector Machines.
  • Execute classification analysis.
  • You will use market-basket analysis and association rules to infer relationships between products.
  • Apply cross validation methods.
  • It will evaluate the predictive performance of classifiers by examining key error metrics.
  • It will optimize the initial performance of a classifier by adjusting its parameters.
  • Interpret the outputs of a classifier and use that interpretation to select between different classifiers based on their performance characteristics.
  • Pre will process data for data mining, for example: apply filters, deal with lost data.
  • Implement feature engineering to improve the performance of a model.
  • It will apply data mining in ecommerce, for example: customer segmentation, recommendation strategy.
  • Present the results of data mining to management.

Course 3: Deep Analytics and Visualization

What will he do in the course

  • It will define the business purpose of a Data Analysis Project and will initially create a realistic analysis plan.
  • Will handle data in R.
  • It will create SQL queries to extract data from an existant database and export it to a CSV file.
  • Will explore the data using visualization techniques and descriptive statistics in R.
  • Choose and evaluate classification modeling techniques in R.
  • Will choose and evaluate regression techniques in R.
  • Analyze data in time series.
  • Execute error analysis.
  • Will interpret a range of performance metrics.
  • It will present highly technical data mining results to a business audience.

Course 4: Big Data: Web Mining.

What will he do in the course

  • It will move business objectives into data mining opportunities.
  • Acquire, process and analyze extremely large data sets using data mining methods to discover patterns or perform data exploration.
  • Install, execute, and apply automatic learning tools to different types of data.
  • It will operate the Amazon Web Services (AWS) cloud computing platform for data analysis.
  • You will discover and assemble extremely large public data sets on the AWS platform.
  • It will configure and execute Elastic Map-Reduce (EMR) and a Hadoop Cluster for data analysis executing a lexical analysis to extract features from web pages.
  • Develop and apply automatic learning models for feelings analysis.
  • Interpret the results of data analysis and data mining to make predictions and establish the reliability of those predictions.
  • It will avoid misunderstandings and errors commonly made when the machine learning methods are applied.
  • It will communicate the results to management and other non-technical audiences.

Skills you will acquire

Upon completion of the data analysis / Big Data program, students will be able to:

  • Identify the types of business problems for which data analysis can provide meaningful information to support business decision making.
  • Translate business objectives into data mining opportunities.
  • Install, execute and apply statistical machine learning tools to different types of data.
  • Apply data mining in electronic commerce, becoming highly competent in the use of statistical techniques of machine learning, such as classification and regression.
  • Acquire, process and analyze extremely large datasets using cloud-based data mining methods to perform data exploration, discover patterns and answer business questions.
  • Visualize data to recognize possible patterns.
  • Interpret the results of the data analysis to make predictions and establish the reliability of those predictions.
  • Communicate the results of data mining to management and other non-technical audiences.


The set of tools is constantly evolving to adapt to changes in the industry. Currently the tools used are the following:

  • WEKA machine learning package.
  • R Statistical programming language and a range of R analysis packages
  • Amazon Web Services Elastic Map Reduce.

Pre requirements

  • At least one year of work experience.
  • Knowledge in Windows, Mac, Linux.
  • Basic knowledge in statistics.
Program taught in:
  • Spanish
  • English (US)

See 2 more programs offered by Universidad Cenfotec »

Last updated May 8, 2019
This course is Online
Start Date
1 - 10 months
5,500 USD
Form of payment: $ 1000 of registration, the balance will be canceled in 9 monthly installments each of $ 500.
By locations
By date
Start Date
End Date
Application deadline