What is the Data Science course syllabus?
Making decisions based on data has become absolutely essential. Having the ability to deeply delve into data, provide insightful insights, and make predictions to address unasked problems is increasingly essential regardless of what level of the organisation we may be working in.
Every job function requires this expertise, and organisations are increasingly looking for future hires and current workers who can handle data. Each of us is working to improve our skills in order to meet market expectations.
There are many Data Analytics courses and Data Scientist courses that help up to learn data-driven skills. Hyderabad more than any other city in India is making a substantial contribution to upskilling the existing and future workforce.
One such company offering data science training in Hyderabad's Madhapur is 360DigiTMG. It is a premier worldwide professional training facility that offers Data Science courses in Hyderabad with a thorough curriculum. The curriculum has been thoughtfully chosen to assist aspiring students in becoming ready for the market. Additionally, the programme is quite helpful for professional advancement, which benefits candidates wishing to upskill in order to advance within the organisation.
Every new cohort is carefully prepared by 360DigiTMG to delve headfirst into the Data Science programme without feeling the slightest bit uncomfortable. Regardless of the participants' academic and professional backgrounds, it starts by assuming they are lacking in fundamental information and builds from there.
First, introduction to project management methodology, CRISP-DM, is introduced to understand how to take care of any Data Science project. Building on project management methodology fundamental concepts of statistics are introduced, which all of us would have learned in 8th to 10th grade during our school days. This module knits through all topics under statistics that we need to understand, which would be required to perform descriptive analysis and build Machine Learning models.
Then, the focus is shifted to hypothesis testing, where participants of the training program learn how to make real-life decisions using data. Also, hypothesis testing also is very critical in understanding Machine Learning models.
The most crucial element of the Data Science programme, where participants study prediction, classification, segmentation, recommendation, data dimension reduction, etc., is machine learning. Supervised Learning, Unsupervised Learning, and Deep Learning are the three types of machine learning that may be used in data science programmes. The Machine Learning journey begins with linear regression models to make predictions after hypothesis testing.
A thorough understanding of the mathematics and algorithms used in the sciences is provided by the discussion of classification techniques like Logistic regression, Multinomial regression, Polytomous regression, K-Nearest Neighbour, Naive Bayes, Support Vector Machine, Decision Tree, and Random Forest, among others. The use of optimisation techniques is essential for developing highly precise solutions. There includes a thorough discussion of several optimisation techniques and their actual use.
Participants learn how to separate the data using clustering approaches using unsupervised learning methods. Learning data dimensionality reduction with the principal component analysis approach. With thorough implementation, network analysis, survival analysis, recommendation systems, and market basket analyses are addressed. Natural language processing and text mining are used to analyse unstructured data.
Learn the core concepts of Data Science Course video on YouTube:
The focus is to understand how to bring textual data to a structured format and then drive meaningful insights. The latest and advanced forecasting techniques are taught using multiple real-life cases.
Introduction to Deep Learning methodology forms the highlight of the Data Science course. Participants get exposure on how to build highly sophisticated Neural Networks to build AI-driven solutions. Introduction to Artificial Neural Network, Convolutional Neural Network, and Recurrent Neural Network is done to encourage participants to enhance their learning. As a Data Science aspirant, one also needs to have hands-on experience with working on relational and non-relational databases to extract and manage data pipelines. For structured data, SQL, and for unstructured data NoSQL training is imparted. Training on Big Data framework, Hadoop, along with Machine Learning on the Cloud, AWS/Azure is also delivered.
Data Visualization is a very critical skill for a Data Scientist that strengthens the Business Intelligence arm of the organization. With Tableau certified trainers, extensive data visualization training is delivered using Tableau. All concepts under the program are backed by business case studies from various industries using R and Python, which are extremely easy to learn even if one has never worked with any programming language. To make the Data Science learning holistic, deployment, and automation of the Data Science solutions are critical. Deployment of Machine Learning models is done by imparting training on Streamlit and Python Flask
At 360DigiTMG, we are aware that automating some processes may save up time, lessen the need for human knowledge, and facilitate the development and use of machine learning models. We have included AutoML in our curriculum for this reason so that our participants may master these tools and technologies.
Without spending hours on data cleaning and preparation, data scientists can swiftly analyse data and provide insights thanks to AutoEDA. To make it simpler to make wise judgements, these tools are made to spot patterns, connections, and abnormalities in the data.
In conclusion, the 360DigiTMG Data Science course in Hyderabad offers advanced training in automated machine learning in addition to covering the foundations of data science. By including AutoML into our curriculum, we make sure that our participants have the most up-to-date tools and methods for working in the Data Science industry.
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