Data Science – Course Summary
Data Science course enables you to understand the practical foundations, helps you to effectively execute and take up Big Data and other analytics projects. The program covers topics from Big Data to Data Analytics Life Cycle. Understanding these topics helps in addressing business challenges that leverage Big Data. Another aspect of this course is that it covers basic as well as advanced analytic methods and also introduces the participant to Big Data Technology and tools like MapR and Hadoop. Our state-of-the-art-infrastructure allows students to understand the applications of these methods and tools by getting hands-on experience working alongside real time data scientists. This program has an open approach including a final lab session which explains various Big Data Analytics challenges by applying the concepts covered during the program in respect to Data Analytics Lifecycle.
The course is designed for anyone who wishes to understand the concepts of Data Science from a Data Scientist’s perspective. Professionals who can benefit from this course –
- Managers from the any field as Analytics is the best tool for managers these days
- Business Analysts and Data Analysts who wish to upscale their Data Analytics skills.
- Database professionals who aspire to venture into the field of Big Data by acquiring analytics skills.
- Fresh graduates who wish to make a career in the fields of Big Data or Data Science
Pre-requisites and Knowledge Skills
The following skill sets and knowledge will enable the students to complete the course successfully and at the same time reap maximum gains –
- Good understanding of basic statistical concepts and a strong quantitative background.
- Knowledge of any scripting languages such as Java, Perl, Python or R, as most of the modules in the course use R, an open-source statistical tool, and programming language.
- Knowledge and experience of SQL
Knowledge of these pre-requisites will enable the participants to understand various advanced tools and methods covered during the program more effectively.
At the end of the course, the participants will be able to –
- Be a part of a data science team and work on Big Data and various other analytics projects.
- Deploy Data Analytics Lifecycle for Big Data projects.
- Changing the frame of a challenge from a business perspective to analytics.
- Understand which analytics techniques and tools will work in a specific Big Data analysis.
- Creation of statistical models and understanding which insights can lead to actionable results.
- Select appropriate data visualizations. This would help in communicating analytics insights to business sponsors and analytics audience in a clearer manner.
- Use various Big Data tools like Hadoop, MapR, R, In-Database Analytics, and MADLib functions.
- Understand how advanced analytics leverage to create competitive advantage. Also, how the roles of data scientists and BI analysts are different from each other.
- What is Data Science?
- Skill-set required
- Job Opportunities
- Continuous vs. Categorical variables
- Mean, Median, Mode, Standard Deviation, Quartile, IQR
- Hypothesis testing, z-test, t-test
Installation of R Studio
- Overview of R Studio components
- Data Structures
- Data Frame
- Slicing and Sub-setting
- Data Frame
Functions in R
- In-built functions
- User-defined functions
Loops in R
Data Import in R
Apply family of functions
Data Manipulation using dplyr
Data Visualization using ggplot2
What is Machine Learning?
Supervised vs. Unsupervised Learning
Exploratory Data Analysis
- Univariate analysis
- Bivariate analysis
- Remove duplication
- Missing value imputation
Underfitting vs. Overfitting
- Assumptions of Linear Regression
- Evaluating Accuracy of model: k-Fold Cross validation
- Confusion Matrix
- ROC Curve
Time Series Forecasting
- Moving Average
- Exponential smoothing
- Holt Winter’s
- Naïve Bayes
- Support Vector Machine
- K-Nearest Neighbour
- Decision tree
- Random Forest
- K-Means clustering
- Introduction to Big Data
- Overview of Hadoop & its Ecosystem
- Introduction to NoSQL
- Overview of Apache Spark
Our instructors are certified professionals and are subject matter experts of Data Science.
To attend the live virtual training, one would require at least 2 Mbps of internet speed.
The candidates need not worry about losing any training session. They will be able to view the recorded sessions available on the LMS. We also have a technical support team to assist the candidates in case they have any query.
The access to the Learning Management System (LMS) will be for lifetime, which includes – Class recordings, presentations, sample code and projects.
Yes, we do have an option of group discount. To know more about group discount, contact email@example.com.
Yes, the course completion certificate is provided once you successfully complete the training program, you will be evaluated on few parameters like – Attendance in sessions, Objective examination and others. Based on you overall performance you will be certified by Collabera TACT.