Nitesh Kumar

Performing data operations like Data manipulation, data visualization, data cleaning, data validation, data modeling, hypothesis testing, creating custom algorithms. Developing data pipeline for the algorithms on server using python, SQL, Cassandra, Kafka, MQTT, API, etc. Develop new data science related products/algorithms like driver scoring algorithm, vehicle scoring algorithm, refuel & pilferage detection algorithm, charging events detection algorithm, etc. Deploying and managing programs on the server. Generating reports to share with clients. Mentoring new joinees or juniors.

Reading Data: We get the data from the client or import data from the server in different formats and if required, we perform merging. Validation: We validate, clean and tidy the data and make it ready to further process with different methods and statistical rules e.g. missing values and outlier detection using histogram, boxplot and IQR x 1.5 range formulae. Analysis: Once we clean the data, then we proceed to analysis, we do the descriptive analysis e.g. mean, median, mode, standard deviation, range, IQR, etc. Once it is done, we share these values to clients as per their requirement. Reporting: Based on client requirements, we create different charts based on cleaned data. E.g. Histogram, bar graph, box-plot, line graph, scatter plot, frequency polygon, frequency curve, pie chart, etc. Analytics: Based on client requirements, we implement the different types of test procedures and modeling procedures to track for the trend and data dependency with the help of different tools. e.g. z-test, t-test, chi-square test, ANOVA, correlation, regression (linear and logistic regression), decision tree, random forest, time series, clustering etc.

Reading Data: We get the data from the client or import data from the server in different formats and if required, we perform merging. Validation: We validate, clean and tidy the data and make it ready to further process with different methods and statistical rules e.g. missing values and outlier detection using histogram, boxplot and IQR x 1.5 range formulae. Analysis: Once we clean the data, then we proceed to analysis, we do the descriptive analysis e.g. mean, median, mode, standard deviation, range, IQR, etc. Once it is done, we share these values to clients as per their requirement. Reporting: Based on client requirements, we create different charts based on cleaned data. E.g. Histogram, bar graph, box-plot, line graph, scatter plot, frequency polygon, frequency curve, pie chart, etc. Analytics: Based on client requirements, we implement the different types of test procedures and modeling procedures to track for the trend and data dependency with the help of different tools. e.g. z-test, t-test, chi-square test, ANOVA, correlation, regression (linear and logistic regression), decision tree, random forest, time series, clustering etc.

Review survey questionnaires and formulate recommendations to ensure efficient programming and optimal data layout. Interact with internal/external clients on project specifications. Advise on realistic timelines for survey programming. Web based survey programming, designing, and development using Confirmit, JavaScript, jQuery, CSS and HTML. Coordinate and consult with the Data Processing Team on best structure in preparation of most efficient data delivery. Performing quality inspection, logic validation, spelling, Survey layout and logical checks on surveys created by me or some other programmers. Understanding research requirements (questionnaire logic, content, and survey layouts etc.). Data analysis for quality check of the survey. Requirement analysis, test planning, functional & module level testing, debugging and maintenance of projects. Efficiently using technical skills to effectively manage projects and meet deadlines.

Review survey questionnaires and formulate recommendations to ensure efficient programming and optimal data layout. Web based survey programming, designing, and development using Confirmit, JavaScript, jQuery, CSS and HTML. Performing quality inspection, logic validation, spelling, Survey layout and logical checks on surveys created by me or some other programmers. Understanding research requirements (questionnaire logic, content, and survey layouts etc.). Data analysis for quality check of the survey. Requirement analysis, test planning, functional & module level testing, debugging and maintenance of projects. Efficiently using technical skills to effectively manage projects and meet deadlines.

Distance Learning / Part time course

Full time course

Full time course

Full time course