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Accelerate your career with the exclusive Data Scientist Master Program. Take advantage of world-class leadership in data science and machine learning that is most sought after. Familiarize yourself with key technologies such as R, SAS, Python, Tableau, Hadoop, and Spark. Become a Data scientist expert today. In this online data science course and 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. the best data scientist.
|Section 1: Introduction|
|Introduction to Startups Details||FREE||00:30:00|
|Introduction to entrepreneurial management Details||00:20:00|
|Section 2: The Ecosystem|
|Different types of entrepreneurship Details||00:30:00|
|Entrepreneurial Ecosystem and Legal Fundamentals Details||01:00:00|
|Section 3: Marketing Startups|
|Value proposition Details||00:40:00|
|Product development Details||00:25:00|
Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
A data scientist is a professional who is responsible for collecting, analyzing, and interpreting huge amounts of data. The role of data scientists is a deviation from various traditional technical roles, including mathematicians, scientists, statisticians, and computer specialists.
This is a comprehensive training course by Eduranz’s Data Science Specialty Camp, which provides in-depth training in data science, data analysis, project life cycle, data collection, analysis, statistical methods, and machine learning. They gained experience in providing references using R programming and studying data analysis, data transformation, experimentation, and evaluation. The demand for data scientists far exceeds supply. Manipulating, Managing and Handling the data is a serious problem in the data-based world in which we live today, 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 course in Data Science will give you all the knowledge you need to study data science, along with data analysis, and Machine learning in R. This will help you track your career to become a more profitable and promising job role, and take your career to the next level.
Programmers, Developers, Technical Leads, Architects
- Developers aspiring to be a ‘Machine Learning Engineer’
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- ‘Python’ professionals who want to learn R and design automatic predictive models in R
The pre-requisites for Eduranz Data Science course require the basic understanding of Computer Programming Languages. Proficiency in any programming language will come in handy for you.
What is Science, the Importance of Data Science in the Modern Digital World, Data Science Applications, Life Sciences Life Cycle, Data Science Life Cycle Components, Introduction to Big Data and Hadoop, Introduction to Machine Learning and Distance Learning, Introduction to R Programming and R-Studio.
Hands-on Exercise – Install R Studio, perform simple mathematical and logic operations with R operators, operator cycles, and case switches.
Introduction to data exploration, importing and exporting data to / from external sources, analyzing data exploration, importing data, data frames, working with data frames, access to individual elements, vectors and factors, embedded operator functions, conditional statements, loop statements, and functions user-defined, Matrix, list and array.
Hands-on Exercise – Access each customer data item to modify, modify, and retrieve recordings with user-defined functions in R.
Data manipulation is necessary, introduction of dplyr packages, selecting one or more columns using the select () function, filtering records based on the filter () function, adding new columns using the mutate () function, sampling, and counting with the sample_n (), sample_frac () functions, & count (), get the summary results using the summarize () function, combining various functions with the pipe operator, implementing operations such as SQL with sqldf.Hands-on Exercise – Perform dplyr to carry out various operations to abstract how data is manipulated and stored.
Introduction to Visualization, Various Types of Graphics, Introduction to Graphics and Grammar Ggplot2, Understanding Categorical Distribution Using the Geom_bar () Function, Understanding Numerical Distribution Using the Geom_hist () Function, Creating Frequency Polygons Using Geom_freqpoly (), Creating Diagrams Using the Geom_bar () Function, Understanding Numerical Distribution Using the Geom_hist () Function, Creating Frequency Polygons Using Geom_freqpoly (), Creating Diagrams Using the Geom_pont () Function, Multivariate Analysis with geom_boxplot, univariate analysis with bar graphs, histograms and density graphs, multivariate distributions, bar charts of categorical variables with geom_bar (), adding topics with topic layers (), visually creating web applications with shinyR, frequency graphs with geom_freqpoly (), multivariate distribution with scattered charts and fine lines, continuous and categorical with chart fields, grouping charts, working with coordinates and topics to make diagrams – representative, introducing topics and various topics, rendering with ggvis packages, geographic rendering with ggmap (), creating web applications with shinyR.
Hands-on Exercise – Create a data visualization to understand the ratio of customer easing based on a diagram with ggplot2. Perfect for importing and analyzing data in a network. You visualize rents, monthly fees, general fees, and other separate columns using a schedule.
Why do we need statistics? Statistical categories, statistical terminology, data types, central trend measures, distribution sizes, correlations and covariates, standardization and normalization, probability and type probabilities, hypothesis tests, quadratic tests, normal distributions, binary distributions.Hands-on Exercise – Create a statistical analysis model that uses quantitative estimates, representations, and experimental data to collect, review, analyze and infer data.
Introduction to Machine Learning, Introduction to Linear Regression, Modeling Predictive Linear Regression, Simple Linear Regression and Multiple Linear Regression, Concepts and Formulas, Assumptions and Diagnostics Remaining in Linear Regression, Building a Simple Linear Regression, Predictive Results, and Obtaining Value-P Introduction to logistic regression, linear regression and comparison of logistic regression, bivariate and variable logistic regression, model confusion and model accuracy, estimated threshold with ROCR, linear reimagration errors and detailed formulas, various assumptions of linear regression, residuals, qqnorm (), qqline (), qqline () ), Understanding model adaptations, building simple linear models, predicting outcomes and determining p-values, understanding common results with the null hypothesis, p-values and F-statistics, building linear models with many independent variables.
Hands-on Exercise – Model relationships in data with linear predictor functions. The introduction of linear and logistic regression in R by creating a model with “downtime” as the dependent variable and several independent variables.
Introduction to logistic regression, the concept of logistic regression, linear versus logistic regression, mathematical logistic regression, detailed formulas, logistic and coefficient functions, logistic regression with two variables, Poisson regression, simple binomial prediction construction, construction accuracy, true positive rate, false positive frequency and confusion matrix for model estimation, ROCR threshold estimation, finding the right threshold with the construct arrangement is the existence of ROC plots, cross validation, and the construction of multivariate logistic regression from logistic models with several independent variables, the real world application of logistic regression.Hands-on Exercise – Apply predictive analysis by describing data and explaining the relationship between dependent binary variables and one or more binary variables. You create a model with glm () and use churn as the dependent variable.
What is classification and various classification techniques, introduction of decision trees, decision tree implementation algorithms, decision tree making in R, perfect decision tree making, confusion matrix, regression tree vs. Tree classification, introduction to tree ensembles and packaging, the concept of Random Forests, the application of Random Forests in R, What Is Naive Bayes ?, Computing probability, Impurity function – Entropy, Understanding the concept of receiving information about the division of the right node. Impurity Function – Get Information, Understand the Gini Node Partition Right Node Concept, Impurity Function – Gini Index, Understand the Entropy Concept of Right Division, Complexity Adjustment, Intersect Tree Decisions and Predictive Values and evaluating performance metrics.Hands-on Exercise – Apply any forest to regression and classification problems. You will build a tree, cut it down, use “churn” as the dependent variable, and build a Random Forest with the right number of trees using the ROCR counter.
What is clustering and what is the use case, what is K-clustering, what is Canopy cloning, what is cluster hierarchy, introduction to unmanaged learning, extraction and grouping algorithms, algorithms for grouping k-resources, theoretical aspects of k means and k means technology flow, K means in R, doing K means to record and find the correct number of scree plot clusters, hierarchical and dendrogram grouping, understanding of hierarchical grouping, implementation in R and Dendrogram Overview, Principal Component Analysis, Explanation of Principal Component Analysis in detail, PCA in R, PCA Application in R.Hands-on Exercise – Use training that is not controlled with R to achieve grouping and resizing. K means grouping to visualize and interpret the results for customer data.
Association Rule Mining & Recommendation Engine
Introduction to the Principles of Mine and Basket Association Analysis, Mapping Rules for Mining Rules: Maintenance, Confidence, Appointment, Apriori Algorithms, and Their Application to Implementation of Item R Based on Recommendation Mechanisms in R, Recommended Cases and Positioned.
Hands-on Exercise – Using association analysis as a rule-based machine learning method to identify stringent rules in a database based on interesting insights.
Introduction to artificial intelligence and in-depth training, which is an artificial neural network, TensorFlow – a computational framework for creating AI models, the basics of creating ANN with TensorFlow in collaboration with TensorFlow in R.
What are time series, techniques and applications, time series components, moving averages, leveling techniques, exponential leveling, one-dimensional time series models, multivariate time series analysis, Arima models, time series in R, sentiment analysis in R (sentiment analysis on Twitter), text analysis.Hands-on Exercise – Analyze time series data, a series of measurements that follow a random disposition to identify the nature of phenomena and predict future values in the series.
Introduction to vector machine support (SVM), SVM data classification, SVM algorithms using single and non-separate cases, linear SVM for hyperplane edge identification.
What is Bayes theorem, what is Naive Bayes classification, classification workflow, how does Naif Bayes classifier work, create a classifier in Scikit-learn, create a probabilistic classification model with Naive Bayes, zero probabilistic problems.
Introduction to the concept of Text Text Mining, Case Text Mining, Understanding and Editing Text with ‘tm’ and ‘stringR’, Text Extraction Algorithms, Text Quantification, Text-Frequency-Reverse-Document-Frequency (TF-IDF), After TF-IDF.
Market Analysis (MBA)
This case study refers to basket analysis analysis modeling techniques, which include information about loading data, various techniques for drawing elements, and running algorithms. Which objects must go hand in hand and can therefore be sewn together. This is used for various scenarios in the real world, for example. B. for shopping basket in supermarket, etc.
In this case, you get a complete understanding of the company’s advertising costs, which help you increase revenue. Logistic regression helps you predict future trends, identify patterns, uncover findings, and more. In this way, future advertising costs can be resolved and optimized for higher revenues.
Multiple regressionYou will learn how to compare MPG per vehicle based on various parameters. You will use multiple regressions and record MPG for brands, models, speeds, vehicle loading conditions, and so on. These include, inter alia, modeling, diagnosis of models, review of ROC curves.
Receiver operation case (ROC)
They work with different data sets in R, apply data exploration methods, create measurable models, predict the results with the best accuracy, diagnose the model that you created with different real-world data and examine ROC and other curves.
Business Context/ 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
1- Research and Development 2- Marketing 3- Employee cost 3- HR and Administration cost 4- Insfrasture cost … etc
Identifying the optimal ratio of the amount of allocation of funds to various segments is of utmost importance. 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)
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, their 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
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.
To run Python, your system must meet the following basic requirements:
1. 32- or 64-bit operating system
2. 1 GB RAM
The statement uses Anaconda and Jupiter notebooks. The E-learning video contains detailed installation instructions.
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.
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. WinPython Portable Distribution is an open-source environment where all the exercises are immediately carried out. Installation instructions are given during training.
This Eduranz Python training course for Data Science gives you hands-on experience in mastering one of the best programming languages, Python. In this Python online course, you will learn basic and advanced Python concepts, including MapReduce in Python, machine learning, streaming Hadoop, and Python packages such as Scikit and Scipy. After successfully completing training, you will receive an Eduranz Attendance Certificate. As part of this Python online course, you work on real-time Python projects that are very important in the corporate world, on step-by-step assignments and curricula developed by industry experts. After completing a Python online course, you can apply for some of the best jobs in the top-ranked MNC in the world. Eduranz provides lifetime video access, tutorials, 24×7 support, and upgrades to the latest version at no additional cost. Therefore it is definitely a one-time investment for a practical Python online course.
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 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.
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.
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 received a certificate that was certified 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.
In our job support program, we help you start your dream job by sharing your resume with potential tenants, helping you make resumes, and preparing you for interview questions. Eduranz training should not be seen as an employment agency or employment guarantee, because the entire employment process is handled directly between the student and the employing company and the final choice is always left to the employer.