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Accelerate your career with the exclusive Online Data Science Course by Eduranz. With this Data Science Certification, take advantage of world-class leadership in data science and machine learning that is most sought after. Familiarize yourself with key concepts such as R programming, data analysis, statistical analysis, data wrangling, data manipulation, data visualization and various machine learning algorithms. Become a Data scientist expert today. In this data science online course certification, you will gain hands-on experience in data science by working on various real-world projects in the fields of e-commerce, entertainment, banking, finance, and others. Get the best Data Science Training from the top data scientists in the industry
|Section 1: Introduction|
|Introduction to Software Training||FREE||00:40:00|
|Object Oriented Design Patterns||00:35:00|
|Section 2: Advanced Computing|
|Introduction to Azure Data factory||FREE||00:25:00|
|Multi Threading in Softwares||00:40:00|
|Managing Software Testing||00:20:00|
|The Software Quiz||00:04:00|
Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
This is a comprehensive Data Science certification course by Eduranz, which provides in-depth data science training, data analysis, project life cycle, data collection, analysis, statistical methods, and machine learning. You will gain experience in providing references using R programming and studying data analysis, data transformation, experimentation, and evaluation. Today, manipulating, managing and handling the data is a serious problem in the data-based world, those who master these skills are mostly in demand. Most organizations are willing to pay the highest salaries to professionals with the right data skills. This online Data Science course will give you all the knowledge you need to study data science, along with data analysis, and Machine learning in R. Also, Data Science Certification will help you track your career to become a more profitable and promising job role and take your career to the next level.
• In this Data Science Course, you will learn about
• Welcome to the analytics world
• Introduction to R- environment
• Importing and reading the data in R
• Data manipulation
• Visualization and reporting in R
• Exploratory analysis in R
• More into analytics world
• Introduction to predictive modeling
• Data preparation
• Cluster and segmentation in R
• Understanding decision trees: Applications in R
• Linear and logistic regression in R
• Understanding the forecasting: Applications in R
• Machine learning in R
- • Freshers or Graduates
- • Anyone willing to have a career in Python
- • If you are working in different programming language (regardless of your experience)
- • Data Analysts or Business Analysts
- • BI Managers or Analytics Professionals
- • Application or Web Developers
- • Software Engineers or ETL Developers
- • Big Data Analysts
- No prerequisites are required for this data science online course, but the basics of any programming language will come in handy for you.
- • Analytics India Magazine reported that the landscape of Data Science is projected to double its size by the year of 2025 (in 2019 it was 3.03 billion). So, this is surely the right time to make a career in Data Science.
- • Data Scientists are the highest paying professionals in the industry.
• This data science course is designed and structured by industry experts, based on industry requirements. Upon completion of this Data Science program an industry recognized certificate is awarded. To learn more, check out the feature section.
According to Indeed, average salary of a Data Scientist is Rs. 8,13,533 per year, in India, which ranges from Rs. 1,46,000 to Rs. 20,43,000 per year. On the other hand, in the US it is $124,220 per year
Online Instructor-led Course
- • Successful completion of all projects, which will be evaluated by trainers
- • Scoring minimum of 60% in the Data Science Online Course quiz conducted by Eduranz
- • Completing all course videos in our LMS
- • Scoring minimum of 60% in the Data Science Course Training quiz conducted by Eduranz
Data Scientists are responsible for extracting meaningful insights from data and interpret them to drive different goals. It requires expertise in tools and concepts from statistics to machine learning. The work cycle of a Data Scientist revolves around collecting, cleaning, and mugging data before being able to process further.
- • Amazon
- • Microsoft
- • Fractal Analytics
- • Maersk
- • Walmart
- • Procter & Gamble
- • Infosys Technology Limited
- • Springer Nature (Source: Indeed) and a lot more
In this chapter, you will get an introduction to the Analytics world along with the following concepts:
Analytics keywords and their definitions
- • SAS Vs R Vs Python
- • Business Objectives
- • Key driving factors in the Analytics world
Case-Study: How Data Analysis is helpful in the Sales Industry
In this chapter, you will be introduction to workspace and environment
- • Useful Default libraries and Packages (Base & other external packages)
- • What is R studio?
- • How to work in other Graphical User interfaces?
Lab-Exercise: Installing R studio and its essential packages along with different GUIs
- • Diving deep into Data Structure & Data Types
- • Vectors
- • Matrices
- • Factors
- • Data frames
- • Lists
- • Importing Data from various sources
- • Database Input: Connecting to database
- • Exporting Data to various formats
- • Viewing Data: Viewing partial data and full data
- • Variable & Value Labels: Date Values
Case study: We will go through a Case Study on HR Analytics
- • Creating New Variables wherein we will perform calculations & binning
- • Working with Operators: Using multiple operators
- • Working with Built-in Functions & User Defined Functions
- • Control Structures
- • Conditional statements
- • Loops
- • Apply functions
- • Sorting, Merging and Appending Data
- • Aggregating/summarizing Data
- • Reshaping & Sub setting Data
- • Understanding Data Type Conversions
- • Sampling
- • Performing File preparation
- • Aggregation
- • Merging
- • Appending
- • Type conversion etc.
- • Renaming and formatting data
- • Handling Duplicates/Missing values
Case-Study: A Case study on Descriptive Analytics based-on Industrial based problem statements.
- • Creating Interactive Graphs on R using packages like
- • GGPLOT
- • GGPLOT2
- • PLOTLY
- • Working with Histograms & Density Plot
- • We will also work with:
- • Dot Plots
- • Bar Plots
- • Line Charts
- • Pie Charts
- • Boxplots
- • Scatterplots
Lab-Exercise: Creating visualization on top of a large dataset using packages like GGPLOT, GGPLOT 2, and PLOTLY and visualize various attributes from the relevant rows.
The chapter where you will learn all the essential topics of Decorators and Generators in Python such as:
- • Diving deep into the basics of Exploratory Data Analysis (EDA)
- • Understanding the spread and data points
- • Understanding the sourced data for better analysis
Case-Study: Working with a Case Study on Exploratory Data Analysis in R using an Industrial-based problem statement
- • Understanding more about Analytics World
- • Data Science Vs Data Analytics Vs Machine Learning Vs Artificial Intelligence Vs Business Analysis
- • Analytics keywords and their definitions
- • Business Objectives
- • Key driving factors in Analytics world
Case-Study: Case Study on how Predictive Analysis is helpful for Sales Industry
- • Types of Business problems
- Mapping of Techniques
- • Different Phases of Predictive Modeling
- • EDA – Exploratory Data Analysis and Need of Data preparation
- • Performing Data Preparation steps
- Outlier treatment
- Flat Liners
- Missing values
- • Dummy Creation
- Variable Reduction
- • Data Alignment and fine tuning
Cluster and Segmentation in R
- • Working with various Behavioral Segmentation Techniques
- • K-Means Cluster Analysis in R
- • Heuristic Segmentation Techniques
- • Value Based, RFM Segmentation
- • Life Stage Segmentation
Case-Study: Case Study on Segmentation Modeling
- • Implementing Decision Tree model in R
- • Understanding steps to perform the Classification based on inferences on Decision Tree
- • Extensive standard R Packages and Functions
Case-Study: Hands-on & Case-Study on Decision Tree Modeling Problem Statements
- • Assumptions of Linear Regression & Logistic Regression
- • Understanding and working with Linear & Logistic Regression
- • Building Linear & Logistic Regression Model
Case-Study: Linear & Logistic Regression Analysis
- • What is Time Series Data?
- • Different components of Time Series data
- • Visualize the data to identify Time Series Components
- • Implement ARIMA model for forecasting
Case-Study: Working on a Case Study of Time Series and Arima Model
- • Understanding differences in Statistical learning vs. Machine learning
- • Understanding essential classes of Machine Learning Algorithms: Supervised vs Unsupervised Learning
- • Text Mining and Sentiment Analysis in R
Case-Study: Performing Machine learning on Sentiment Analysis
Mini-Project: Loan Prediction
Problem Statement: A major government bank has approached your company to analyze from their customer loan data set. Since the last 4 months, a lot of customers who are not able to repay their loan amount has increased. You have been assigned a task of analyzing from the data set and give insights about which customer should be given the loan approval and which shouldn’t be.
Topics: Data Preprocessing and Model Building
Business Objective: To develop a Machine learning algorithm to identify the most optimal ratio/ aspects to allocate funds/ spending proportionately by organizations in different areas of expenses like:
- • Research and Development
- • Marketing
- • Employee Cost
- • HR & Administration Cost
- • Infrastructure Cost etc.
Identifying the optimal ratio of amount of allocation of funds to various segments is utmost important. This would also help the Management team with below aspects:
- • To increase the revenue and profitability
- • To better design the Marketing strategies
- • To allocate the internal resources better
The algorithm would help in identifying the relationship between profit and various types of expenses individually by an organization (as mentioned above).
Scope of Approach:
- • Within this final project of Data Science using R, we will include all the essential steps which a typical Machine learning based project should have with Multiple Regression.
- • The data will be having multiple sources and format mainly having details about various organizations, there profit and their expenses in different areas.
- • We will use the bulky input data set to be imported from different sources, the combined rows will contain 2k+ rows (tentative) with all the required column.
We will include the following technical dimensions while undergoing the project:
Part 1: Reading and importing the dataset
We will use all the technical steps requires to read the data from different sources and formats like:
- • CSV Files
- • Excel Files
- • SAS Datasets
Part 2: Data Combination
The steps required to append or merge the different datasets into one
Part 3: Data Preparation
Steps including various data complexities like:
- • Missing value treatment
- • Outlier Treatments
- • Duplicate rows and entries etc…
Part 4: Algorithm/Model Building
We will try to include two or three different techniques of Data Science over the dataset to give the feel and flavor of actual project
Part 5: Inferences
Making and implementing the references based on the figures captured.
At the time of enrollment team Eduranz will assign one mentor for you and he will be guiding you in this lifetime Journey.
24/7 Tech Adviser Support
Lifetime 24/7 Technical and Non Technical Support from team Eduranz.
Get Lifetime opportunity to access and attend the live sessions multiple times.
Assignments & Quizzes
Every module will be followed by certification based assessment and quiz.
Become a Certified Professional.
Team Eduranz will update your Resume before forwarding it to our 60+ global Clients.
No one misses any lecture at Eduranz, because you will be provided with the recorded sessions of the class on your LMS withn 24 hours and despite that, you can also attend any different live session to cover up the missed topic and aks your doubts from the trainer.
Live Virtual Classes or Online Classes: With online class training, you can access courses via video conferencing from your desktop to increase productivity and reduce work time and personal time.Independent study online: In this mode, you will receive videos with lectures and can continue the course as you wish.
Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months intensive industry experience.
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them underwent a rigorous selection process that included screening profiles, technical assessments, and training demonstrations before being certified for training. We also ensure that only high-level graduates live in our faculty.
Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing training. We also don’t limit the number of tickets you can collect when solving questions and doubts.
Eduranz offers independent learning for those who want to learn at their own pace. This training also gives you the benefits of email questions, tutorial sessions, 24×7 support, and access to modules or LMS for lifelong learning. In addition, you will receive the latest version of learning material at no additional cost. Independent Eduranz training is 75% lower than teacher-led online training. If you experience problems while studying, we can arrange virtual courses directly with the trainer at any time.
Eduranz actively supports all trainees who have successfully completed the training. That’s why we are involved in more than 80 top MNCs worldwide. This way you can be in exclusive organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco and other similar-sized companies. We also support you during job interviews and preparation of your CV. Is it possible to switch from independent training to teacher-led training?In any case, you can switch from self-directed self-training to online training only by paying an additional amount and participating in the next set of training that will be specifically notified to you.
After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; You will receive an industrial recognized certificate by Eduranz. This certification is recognized by Eduranz’s partner organizations, which includes a lot of top MNCs worldwide that are also part of the Fortune 500 list.
Our job assistance program will help you reach the job that you have been seeking. In our support program, we help you by sharing your resume with the companies that we have tie ups with, along with helping you in resume building, getting your prepared for the interviews through mock sessions by our industry experts (from various companies like IBM, Microsoft, Accenture, Delloite etc.), also by providing you with mock interview questions and exhaustive session by Eduranz. However, Eduranz is not a recruitment or job agency, we do not guarantee you a job, we simply direct your resumes to different companies, then after that, entire process is handled by the employer and company and the result is totally based on the employer’s decision.