Qualimations Data Science takes your through extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Analytics at Qualimations is more than just analytical methodologies or techniques used in logical analysis. It takes you through a practical process of transforming data into actions through analysis and insights in the context of organizational decision making and problem solving. The Analytics includes a range of activities, including business intelligence, which is comprised of standard and ad hoc reports, queries and alerts; and quantitative methods, including statistical analysis, forecasting/ extrapolation, predictive modelling (such as data mining), optimization and simulation.
These techniques enable designers/analyst to determine simultaneously the individual and interactive effects of many factors that could affect the output results in any design. The analytical techniques also provides a full insight of interaction between design elements; therefore, helping turn any standard design into a robust one. The Data Science training lets you gain expertise in Machine Learning Algorithms like Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning.
In brief this course supports to spot the sensitive parts and sensitive areas in your process that cause problems in Yield, revenue,cost. Analyst will be able to fix them and produce robust and higher yield designs prior going into production.
Learn from real time experts and with actual data
The exposure to many real-life industry-based projects which will be executed in RStudioThe objectives of this course is as follows.
- To gain an understanding of how analyst and managers should use R and Python to formulate and solve business problems and to support revenue enhancement and managerial decision making.
- To become familiar with the processes needed to transform data and develop exception based learning and analysis of business data.
- Factorial analysis provides a cost-effective means for solving problems and developing new processes. To learn how to use and apply various data analysis using machine learning algorithms.
- Statistical data Analysis and inference
- One /two and multiple Factor Experiments for business and science.
- Model evaluation, errors, entropy and Equations.
- Residual Analysis, Probability plots, trends and PCA analysis .
Methods for Analytics training and certification
"Method: In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.
This course stresses the factors and uses R to study performance of business decision makers and the data management and analysis methods that have value to them. This course includes lectures, presentations, and demonstrations that emphasize discussion and illustration of methods, as well as hands-on, practical exercises that provide both a sound base of learning and an opportunity to test and develop skill. The use of software supports the presentation of the material. Candidates participate in exercises ,project discussions. You will be expected to complete a projects that apply analytics principles and techniques to a business problem.
" Qualimations core certification program is the best suited to do so"...
- Download/Installing and R/Studio/CRAN
- Getting data into R and basic transformation
- Data Structures,frames,matching, outliers and handling errors
- Basics of R syntax
- Data wrangling,merge and exploration, ggplots, scatter
- Classification and Machine Learning
- Importance of data quality .
- Data Structures and decision support
- Text Mining, ANOVA, Deep learning
- Time series Analysis
- Principal compenents and features engineering
- Analytics and Software support
- Data Mining Process
- Market Data Analysis
- Data Analysis and Statistic
- Classification and Regression Trees
- Data trends and effectiveness
- Marketing mix, capital budgeting, portfolio optimization
- Decision Making under Uncertainty
- Predictive Analysis
- Building Apps using R
- Perform feature engineering to create predictors
Become a Data Scientist
R and Python are a great programming language to compute Big Data. This course allows you to gain insight into the 'Roles' played by a Data Scientist. Reams of base data are now available as part of finance, hr, production and quality could be acquired and used for business development. This data is many times available as part of the ERP systems and its database. R could now sit on a layer of this base data and perform all the heavy, resource intensive simulations and hypothesis testing.
R will help you handle large complex data sets, i was personally involved many times in trying to infer from sparse data and a sea of zeros, it still gives good inferences. But what if you’re new to this field of analytics? Where do you begin? What do you need to learn to get started and find your footing in Data Sciences? R can certainly be an area to start and a tool in automating, thus lighten the load of hard-working humans, particularly in the typical data-intensive processes, learn these methods from the experts at Qualimations
Data Science Life Cycle
Download, install, and use the R platform Import and visualize data in R
Techniques for Data Transformation,Use vectors and vectorized functions, including some of the tapply functions Manipulate data into desired formats
Data Mining techniques and their implementation
Analyze data using Machine Learning algorithms in R
Explain Time Series and it’s related concepts
Data Visualization and Optimization techniques
Do actual application project as you learn
Apply in real life problems and Make data-driven decisions about your industrys
This course give a deep introduction to machine learning techniques using several popular classification algorithms. You will have substantial experience with R programming by this time, with some knowledge of machine learning, its application and implementation through the hands-on use cases. Each algorithm is briefly explained using real time samples, and links are provided with more in-depth analysis.... Read More
As part of the Business Analytics training course we provide you the following: