Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Course Overview
The Professional Certificate Programme in Advanced Data Analytics for Managers online course is suitable for those individuals with a keen sense of learning and building a strong career in analytics. This course specifically aims at delivering managerial insights to the participants. Topics such as Machine learning, Data visualisation, Data analytics will be covered using R. The application and usage of data analytics in business is a new phenomenon leading to the creation of new business strategies. These new business strategies will be analyzed and comprehended by the participants.
The usage of data analytics in business also has some problems. This programme seeks to ensure detailed causes and explanations of these problems with real-life case studies and practical sessions. The classes for this programme are scheduled every Saturday from 3 pm to 6 pm. The instructors and the students will meet online via the zoom platform.
The Professional Certificate Programme in Advanced Data Analytics for Managers is verified by the Indian Institute of Management Kozhikode. The participants will be benefited from the Certificate of completion on successful completion and a participation certificate on unsuccessful completion. This certification will help working professionals gain new heights in their careers.
The Highlights
- Certification by IIM Kozhikode
- Alumni status of IIM Kozhikode on completion
- Duration of 10 months
- Completion & participation certification
- Online live sessions
- Weekly 3 hours of effort
- Sessions only on Saturdays
- Installment wise fee collection
- Live classes on zoom
Programme Offerings
- Case Studies
- Live online lectures
- assignments
- Capstone Project
- Quiz
- 10 Months Programme
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIM Kozhikode |
The participants can check these detailed Professional Certificate Programme in Advanced Data Analytics for Managers fees structure-
- The total fee for this programme is Rs. 1,96,000 with GST.
- The participants for the application fee will have to pay Rs. 2,000 with GST.
Fee category | Amount in rupees |
Programme fee | 1,96,000+ GST |
Application fee | 2,000+ GST |
Eligibility Criteria
Work experience
A minimum of one-year work experience after graduation is required for the Professional Certificate Programme in Advanced Data Analytics for Managers training.
Education
The candidates who have successfully graduated or have a diploma in any field from a recognized university is required.
Certification qualifying details
The candidates will have to maintain 75% of attendance during the course. The candidates will also have to pass in the periodic evaluations such as assignments, quizzes, projects and case studies. After completing all these criteria, the candidates will receive the prestigious Professional Certificate Programme in Advanced Data Analytics for Managers by IIM Kozhikode, and Eruditis.
What you will learn
The Professional Certificate Programme in Advanced Data Analytics for Managers syllabus includes all the relevant and implementable tools for successful managerial endeavours. These are the specific learning outcomes from this course-
- An in-depth understanding of data analysis and its structures will form.
- The introduction and application of data analytics tools such as R, ggplot2 is included in the programme.
- The working mechanisms of data science for business purposes will be explained.
- How text mining is used for unstructured data formats will be practically taught to the participants.
- Social media analytics usage is also a part of the Professional Certificate Programme in Advanced Data Analytics for Managers training.
- This programme is mainly focused on shaping the participant's managerial outlook.
- The participants will understand various nuances in the analytical skills and learn to differentiate the predictive, descriptive and prescriptive analytics.
- How to use data visualization and understand the relationships between datasets will be taught.
- The participants will understand how the success of data analytics in business is related to revenue- generation.
- How the insights of revenue-generating data analysis in business are used for predicting future business strategies will also be taught in Professional Certificate Programme in Advanced Data Analytics for Managers syllabus
Who it is for
The following candidates will be profited by Professional Certificate Programme in Advanced Data Analytics for Managers benefits-
- The working professionals who are in search of better opportunities in data science and business analytics can apply in the course.
- The professionals who are already in managerial positions and are hoping to improve their skills for further career growth can apply.
- The participants who are working as business analysts and hope to become efficient in data analytical roles can apply.
- Consultants, Senior managers, and leaders of business organisations who are keen on leveraging their business strategies can apply.
- Recent graduates or diploma holders with a minimum of one year of experience from any discipline can apply if they have interests in data science.
Admission Details
The participants can check these given guided steps for Professional Certificate Programme in Advanced Data Analytics for Managers classes-
Step1- Click this URL for this course- https://www.eruditus.com/india/programmes/professional-certificate-programme-in-advanced-data-analytics-for-managers/?redirect=pardot
Step2- Then the course website will appear.
Step3- In the Professional Certificate Programme in Advanced Data Analytics for Managers by Eruditus, there will be a “Download Brochure” option. Fill in your name, country, mobile number, email id and then download to access the course contents.
Step4- After going through the course content, for admission click on the “Apply Now” option.
Step5- For proceeding further, fill up the required email id, password and click on the submit button.
Step6- Then the fee will be collected in instalments for which online payment will be the form.
Step7- After completing all these steps, the participants can start attending zoom classes on Saturdays.
The Syllabus
- Introduction R environment
- IDE-R studio
- Installing packages and loading packages in R
- Creating variables
- Scalars, vectors & matrices
- List, data frames & data types
- Converting between vector types
- Cbind & Rbind
- Attach and detach functions
- Reading .csv and .txt files
- Importing data from excel
- Loading and storing data with a clipboard
- Saving in R data, loading R data objects
- Writing data into the file
- Writing text and output from analyses to file
- Rmarkdown
- Data subsets
- Selecting rows/observations
- Rounding a number
- Creating a string from variable
- Factor labels
- Selecting columns/fields
- Merging data
- Relabelling the column names
- Data sorting, data aggregation, and finding and removing duplicate records
- Application of dplyr package (select, arrange, mutate, aggregate, summarise, and group)
- Basics of data visualisation using ggplot2
- Aesthetic mappings
- Common problems
- Facets
- Geometric objects
- Position adjustments
- Coordinate systems
- The layered grammar of graphics
- Combining plots
- Execution of various types of plots (box plot, histogram, pie chart, line chart, scatterplot, word cloud, probability plots, mosaic plots, correlograms, and interactive graphs)
- Data cleaning
- Handling missing data
- Data imputation
- Feature filtering
- Categorical feature filtering
- Identifying misclassifications
- Data transformation
- Min-max normalisation
- Z-score
- Standardisation
- Decimal scaling
- Transformations to achieve normality
- Outliers
- Graphical methods for identifying outliers
- Numerical methods for identifying outliers
- Flag variables
- Transforming categorical variables into numerical variables
- Binning numerical variables reclassifying categorical variables
- Adding an index field
- Removing variables that are not useful
- Data balancing techniques
- Hypothesis testing versus exploratory data analysis
- Getting to know the data set
- Exploring categorical variables
- Exploring numeric variables
- Exploring multivariate relationships
- Selecting interesting subsets of the data for further investigation
- Using EDA to uncover anomalous fields
- Binning based on predictive value
- Deriving new variables: flag variables
- Deriving new variables: numerical variables
- Using EDA to investigate correlated predictor variables
- Need for dimension-reduction in data mining
- Principal components analysis (PCA)
- Application of PCA
- Statistical inference
- Confidence interval estimation of the mean
- The margin of error
- Confidence interval estimation of the proportion
- Hypothesis testing for the mean
- Assessing the strength of evidence against the null hypothesis
- Using confidence intervals to perform hypothesis tests
- One-sample t-test
- Paired sample t-test
- Chi-square test for goodness of fit of multinomial data
- Analysis of variance (ANOVA)
- Supervised versus unsupervised methods
- Statistical methodology and data mining methodology
- Cross-validation
- Overfitting
- Bias-variance trade-off
- Balancing the training data set
- Establishing baseline performance
- Simple regression analysis
- Model formulation
- Verifying the regression assumptions
- Inference in regression
- Multiple regression analysis
- Dummy variable
- Stepwise regression analyses
- Naïve Bayes and Bayesian networks
- Application of logistic regression for estimation and prediction
- Neural networks for estimation and prediction
- Random forest
- Decision tree
- K-nearest neighbour algorithm
- Market basket analysis
- Affinity analysis
- Measuring cluster goodness
- k-means clustering
- Hierarchical clustering methods
- Social network analysis
- Lexicon analysis
- Social media analytics (Twitter)
- Text mining and sentiment analysis