Machine learning problems

  • Machine learning and neural network technologies are overrated.

  • Machine learning methods reduce the culture of analytical thinking.

  • To achieve results in projects involving data analysis, knowledge of the subject is more important than deep knowledge of Machine learning.

  • The profession of Data Scientist is greatly overrated, generalists are gradually disappearing.

Technology overrated

  • Most of the problems that can now be solved with the help of modern methods of data analysis and neural networks have been solved for a long time.

  • The tasks are essentially not new. Analysts who understand the subject area participate in their decision.

  • Often, machine learning algorithms in such systems are already in place.

  • To do something fundamentally new and really applicable here is extremely difficult.

  • “The apples that fell from the tree are already harvested.”

Analytical thinking

  • You need to deeply understand the subject area:

    • what data is needed;
    • are any predictive algorithms needed;
    • is it possible to verify the prediction.
  • Requires an analytical approach.

  • Requires a culture of working with data.

  • Requires the ability to put hypotheses.

More important knowledge of the subject

  • The disadvantages of a typical Data Scientists include:

    • almost do not ask any questions;
    • data and so will tell about everything;
    • use some arbitrary data;
    • They say that they built some kind of model.
  • The result cannot be verified.

Universal specialists will no longer be

IMHO, an effective Data Scientist

  • can not be a generalist;
  • must be an expert in the subject area.

Hype is over


Data science is not rocket science

Project structure

How the data analysis project works

  • Project requirements
  • Project data
  • Development and implementation of the project


  • We initially do not know anything about what data we have.
  • We need to understand the statement of the problem.
  • We must understand what result is required to get from the project.
  • We must decide by what method the problem can be solved.
  • We need to set data requirements.


  • Search for data to solve the problem:
    • we will find out what sources are available to us;
    • we form a sample with which we will continue to work.
  • Data research:

    • explore the central position and variability;
    • identify correlations between signs;
    • build distribution schedules.
  • Data preparation.

Development and implementation

  • Model development.
  • Software implementation of the model.
  • Run training set.
  • Testing on a test sample.
  • Verification of the result.
  • Loop (you can start all over again).


Understanding of the task

  • It is necessary to clearly define the purpose of the study.
  • What is the problem?
  • What metrics will measure success?

The choice of analytical approach

  • If you need a yes / no answer, a Bayesian classifier is suitable.
  • If you need an answer in the form of a numerical sign, then regression models are suitable.
  • If it is necessary to determine the probabilities of certain outcomes, it is necessary to use a predictive model.
  • If you need to identify relationships, a descriptive approach is used.

Data requirements

  • What data will give the desired answer?
  • Data requirements:
    • content;
    • data formats;
    • data sources.


Data collection

  • We collect data from available sources.
  • We make sure that the sources:
    • available;
    • reliable;
    • can be used to obtain the required data in the required quality.
  • It is necessary to understand whether we received the data we wanted.
  • Revision of data requirements.
  • Deciding on the need for additional data.
  • Finding a replacement for missing data.

Data analysis

  • Are the collected data representative of the problem?
  • Descriptive statistics apply to all variables that will be used in the selected model:
    • the central position is studied (middle, median, mode);
    • emissions are searched for and variability is estimated (variance, standard deviation);
    • histograms of the distribution of variables are built;
    • other visualization tools are used (for example, boxes with a mustache).

Data analysis

  • Correlations between variables are calculated.
  • If there are significant correlations between the variables, some variables may be discarded as redundant.

Data preparation

Data collection and analysis + data preparation = 70%–90% of the project time.

Data preparation

  • We process the data in such a way that it is convenient to work with them:
    • remove duplicates;
    • process missing or incorrect data;
    • we check and correct formatting errors.
  • We are designing a set of factors that machine learning will work with in the next steps:
    • feature extraction;
    • feature selection.
  • Errors at this stage can be critical.
    • Excessive number of characteristics = model retrained.
    • Insufficient number of signs = model is under-trained.

Development and implementation

Model construction

When the type of model is defined and there is a training sample, we develop the model and test it on a set of features.

Model application

  • Calculations alternate with model setup.
  • Does the constructed model meet the original task?


  • Diagnostic measurements are taken to help determine if the model works as intended.

  • The statistical significance of the hypothesis is checked.

  • It is necessary to make sure that the data in the model are correctly used and interpreted and the result obtained does not go beyond the limits of statistical error.


  • Implementation is carried out in stages:

    • a limited group of users;
    • test environment.
  • Feedback system.