This course will teach you the fundamentals of data science, from data collection and preparation to data analysis and modeling. You'll also learn how to use data analytics to solve real-world problems.The course is offered both online and in-person, and it provides a variety of practical applications that you can apply right away. You'll gain a solid understanding of using data to improve business processes, make better decisions, and develop insights that can lead to more success.
November 5th
MON - FRI (45 DAYS)
Timings - 08:30 PM to 10:30 PM (IST)
November 15th
MON - FRI (45 DAYS)
Timings - 08:30 PM to 10:30 PM (IST)
December 1st
MON - FRI (45 DAYS)
Timings - 08:30 PM to 10:30 PM (IST)
You can attend our Service Now online classes from any part of the world, at any given time.
You can attend our in-class Service Now training from the nearest branch in your area.
Learn at your own pace from the comfort of your home. All the classes will be recorded with lifetime access to it. You can watch and learn the concepts at your own schedule.
Clear understanding of what data science
Get started on a successful data-driven career.
The course is comprehensive and challenging, and it provides you with the skills and knowledge you need to pursue a career in Data Science. The Gautham Digital Learning Data Science course is designed for anyone who wants to enter the field of Data Science. It covers both the theoretical and practical aspects of the Data Science field and provides students with the skills and knowledge needed to become successful data analysts. The course is divided into four modules, each of which covers a different area of Data Science.
The modules focus on data pre-processing and include data cleansing, data profiling, and data preparation. Further modules cover data analysis techniques, including linear regression, multiple linear regression, and decision maps. Then other modules cover statistical inference and focus on hypothesis testing, analysis of variance, and linear regression models. This is how the modules are covered with a lot of expertise from our instructors.
Our expert trainers have years of experience in the field and are passionate about teaching you the ropes. Whether you're a beginner or an experienced data professional, our course will help you unlock the power of data for your business.
With our hands-on exercises and real-world examples, you'll be able to use data to improve your operations and boost your bottom line.
In addition, our online platform makes it easy for you to follow along and ask questions whenever you get stuck. Our course is designed with practicality, ensuring that you understand the techniques and concepts at hand.
Moreover, our expert trainers will keep you engaged and motivated all the way through, ensuring that you gain the skills you need to stand out from the pack.
You'll get to apply what you've learned in class by completing challenging exercises and projects.
By the end of the course, you'll have learned everything from data exploration and analysis to modeling and machine learning.Â
There are various career options that you can pursue after completing a data science course.
Some of these include becoming a Data Analyst, Data Scientist, Data Engineer, or a big Data Engineer.The eligibility criteria for the course is that you should have an undergraduate degree in computer science or mathematics for better comprehension of Data Science.
After completing a Data Science course, you can pursue a career in the following fields:
- Analytics
- Business Intelligence
- Data Mining
- Data Science
- Machine Learning
Eligibility for the course depends on your level of expertise in computer programming and statistics.The course is designed for people with an undergraduate degree in computer science or statistics.
Data Science is a rapidly growing field that is having a significant impact on many industries. This field entails the application of analytics and computer science techniques to manage, understand, and act on large data sets. Gautham Digital Learning offers comprehensive training in Data Science to help students gain the skills they need to excel in this field.
Data Science is a rapidly-growing field that deals with the manipulation, analysis and interpretation of large data sets in order to make better decisions. It's a highly versatile skill that can be used in a variety of industries, such as finance, marketing, healthcare, and retail.
According to the recent report by the National Institute of Standards and Technology (NIST), the data science field is expected to grow at a rate of 29% over the next five years. This means that there will be a lot of opportunities for people who are interested in this field.
If you're interested in learning more about data science, there are a number of resources available online. One of the best ways to learn is by participating in online courses or workshops. There are also plenty of books available on the topic that can help you learn more about how to use data to make better decisions. Gautham IT provides a variety of courses and tutorials that cover data science topics such as machine learning, natural language processing, and big data management.
Our Data Science course is designed to equip you with the skills and knowledge you need to succeed as a Data Scientist. By following the concepts, applications and expertise taught in the course, you'll be well on your way to becoming a data-driven professional! By now you have a clear understanding of what data science is and what it can do for your business.
 If you're interested in learning this field and want to gain the expertise required to power your analytics and decision-making, we highly recommend you enrol in our course! Enrolling today is your best chance to get started on a successful data-driven career.
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• Probability & Statistics.
• Multivariate Calculus & Linear Algebra.
• Programming, Packages and Softwares.
• Data Wrangling.
• Database Management.
• Data Visualization.
• Machine Learning / Deep Learning.
• Cloud Computing
Data science is extracting meaning from data to make informed decisions. It involves using analysis, modelling, and prediction techniques to understand the patterns that exist within data sets. A vital aspect of this field is understanding how different factors (such as user behaviour or customer insights) impact business outcomes.
The second main concept of data science is machine learning. This refers to artificial intelligence (AI) and computer models to automate decision-making processes by improving efficiency and accuracy over time. Machine learning can be used for various tasks, including recognizing text or images, predicting trends, identifying frauds or abnormalities in large datasets, and more.
Finally, another important concept in data science is big data—the storage and processing capacity required to manage increasingly complex datasets with high levels of heterogeneity (different types/roles/values). Big data has come into prominence due to its potential role in developing intelligent algorithms that can identify patterns hidden within vast amounts of historical information from social media feeds or other web sources.
Top Data Science Tools
1. SAS. It is one of those data science tools designed explicitly for statistical operations.
2. Apache Spark. Apache Spark, or simply Spark, is an all-powerful analytics engine, and it is the most used Data Science tool.
3. BigML.
4. D3.
5. MATLAB.
6. Excel.
7. ggplot2.
8. Tableau.
Data science is all about understanding and utilizing data to make informed decisions. However, it’s essential not to forget the importance of intuition when working with data. Intuition helps you identify patterns and trends that may be difficult to see. Additionally, it can help you develop hypotheses about how various factors might influence your data.
When doing Data Science, you need both quantitative (statistical) and qualitative skills to produce high-quality insights. A strong foundation in mathematics and statistics is essential for analyzing your data using models such as linear RegressionRegression or logistic RegressionRegression. Good Python programming skills allow you to create these models more efficiently and interactively explore the results.
In simple terms, a data scientist’s job is to analyze data for actionable insights. Specific tasks include: Identifying the data-analytics problems that offer the most excellent opportunities to the organization—determining the correct data sets and variables.
While data science may be a challenging job, it is not necessarily stressful. Many people find the challenge and excitement of working with data stimulating and motivating. What are the primary duties of a data scientist? The tasks that fall under this category include developing models for predicting future outcomes, designing experiments to test hypotheses, discovering new insights from large datasets, understanding how customers behave to improve customer experiences, etc.
The responsibilities of a data Scientist often involve working with various types of software, including algorithms (to carry out predictive modelling), programming languages (for development purposes), databases (for storing collected information), and text mining toolkits as visualization tools). As you can see, there is hardly anything mundane or routine involved! So while it might require some creativity and think outside the box at times- which is what makes this career so exciting- overall stress levels tend to remain low.
Logistic regression is a predictive modelling technique that can investigate the relationships between variables. It uses mathematical models to estimate the probability of an event happening, given specific values for those variables. This information can then be used to make predictions about future events.
The benefits of using logistic regression include its ability to identify patterns in data and its ability to generate accurate forecasts. Additionally, it is flexible enough to account for both discrete and continuous predictors.
The term “Data Science” has been used to describe a range of different activities concerned with the analysis, interpretation, and application of data to make informed decisions. This can include understanding customer behaviour, predicting trends, or improving current business processes.
It is important to remember that Data Science is not a single discipline or expertise area; it is an approach that incorporates many different skills and techniques. It may also involve working with computers to create powerful analytical tools. Therefore, if you’re interested in developing your data-related abilities, then be sure to take courses or enrol in online programs that will teach you the basics of this fascinating field.
• Data analytics is transforming raw data into information that can be used for decision making.
• On the other hand, data science is a more comprehensive approach that considers the entire data journey from acquisition to analysis to action.
Linear regression is a technique in which the score of a variable Y is predicted using the score of a predictor variable X. Y is called the criterion variable. Some of the drawbacks of Linear Regression are as follows:
The assumption of linearity of errors is a major drawback.
It cannot be used for binary outcomes. We have Logistic Regression for that.
Overfitting problems are there that can’t be solved
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