Data Science Certification
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.
Who can learn Data Science?
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 for Data Science Course:
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.
Training on Data Science Course Objective
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.
- Main objectives of a Data Science Project
- What you need to learn to become a Data Scientist?
- What are the opportunities and Prerequisites?
- Target Audience
- CRISP – DM Process Model
- CRISP – DM Phases
- Random Variable
- Central Tendencies – Mean Mode, Median
- Probability, Probability Distribution of Random Variables
- Type of Random Variables – Based on Scale of Measurement
- Variance & Standard Deviation
- Normal Distribution
- Standard Normal Distribution and Z-Score
- Binomial Distribution
- Poisson Distribution
- Inferential Statistics
- Sampling Distribution
- Central Limit Theorem
- Hypothesis and hypothesis Testing
- Type I and Type II Errors
- Hypothesis Testing using z-test
- Hypothesis Testing t-test
- Introduction to R Programming
- Data Structures in R
- Slicing and Dicing the data
- Control structure and Loops in R
- Basic Inbuilt R Functions
- User Defined Functions
- Apply Family of Functions
- Data Manipulation using ‘dplyr’ Package
- Graphics and Data Visualization in R
- Exploratory Data Analysis in R
- ggplot2 Package
- Applied Statistics using R
- Supervised & Unsupervised ML
- Supervised ML Models
- Linear & Logistic Regression
- Regression methods
- K Nearest Neighbours KNN
- Decision Tree
- Random Forest
- Unsupervised ML Models
- K Means Clustering
- Model Evaluation
- Under fitting and Over fitting
- Confusion Metrix
- K-Fold Cross Validation
- Regression Evaluation Metrics
- Time Series Analysis
- Support Vector Machine (SVM)
- Naive Bayes
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 firstname.lastname@example.org.
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.