Data scientists must know how to code - start by learning the fundamentals of two popular programming languages Python and R.
Once you have the core skill of programming covered – dip your feet in the nitty-gritties of working with data by learning how to wrangle and visualize them.
It is impossible to use data without knowledge of statistics. Collect, organize, analyze, interpret, and present data using these concepts of statistics.
The content includes applied aspects of artificial intelligence: 20+ practical assessments to strengthen learning alongside clear, targeted and actionable feedback. 5+ end-to-end case studies supported real-world business problems across varied industries that offer students a style of real-time expertise.
Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.
Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib and Tableau.
Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.
Lastly, manage your infrastructure with a data engineering platform like Spark so that your efforts can be focused on solving data problems rather than problems of machines.
The course culminates in an enterprise-level project for a fictitious client that will expose you to every stage of the data science process – from data acquisition and preparation to evaluation, interpretation, deployment, operations, and optimization. Every student is guided by industry experts as they bring their personal projects to life. Alternately, students may choose to work on a live project from their organization. Our students generally come from varying backgrounds. We encourage all our students to pursue projects that are best suited for their careers and domains. The project is an opportunity for you test your skills and demonstrate your ability to invent solutions for real world problems.
After completing your assignments and projects, we will provide you with one-on-one career guidance, conduct mock interviews and help you build a professional online portfolio to help you get noticed by top recruiters.
Learn what to put in your resume and how to get noticed on top job portals from industry experts.
Establish your presence on all the right social networks like Git, Stack Overflow, LinkedIn, etc
Get insider tips on how to ace interviews at top firms.
Work at top MNCs and start-ups through by putting your best foot forward in our placement drives.
Data from commercial research has always had a say on how manual tasks are conducted in the pharmaceutical industry. With advancements in machine learning and artificial intelligence, data now plays an even more significant role in prescribing the best medicines for very specific medical conditions. Prescribing the right medicine is essentially a classification problem. At Acadgild, we focus on Image Analytics, NLP, Text Analysis and Predictive Analytics using CART (Classification and Regression Tree) models.
Data is arguably most influential in the e-commerce industry. Needless to say that machine learning and artificial intelligence play a crucial role in the development of this domain. These technologies help gather, analyse, and make sense of data regarding customer habits in real-time to make suitable predictions. For instance, the moment someone adds a product to their cart, websites and e-commerce platforms can recommend other products that the same customer is most likely to buy thereafter. Machine learning and artificial intelligence is also useful in anomaly detection, customer care and pricing. Acadgild issues several case studies that involve churn predictions, sentiment analysis, customer segregation and time series forecasting – all of which are crucial in e-commerce.
Data science relies on information from a variety of sources to make effective predictions. In a well-documented domain like finance, it has created plenty of opportunities to understand customers and deliver better products and experiences. Tasks like generating a credit-score for potential customers has become a lot easier. Acadgild uses case studies from finance to help learners understand the practical implementations of feature engineering techniques and other machine learning concepts to solve real-world problems.
Machine learning is transforming the telecommunications industry. It is having a significant impact in network automation, and more importantly, on customer experience. The technology is also reducing operational costs and helping telecom companies achieve greater margins with increased efficiency. Acadgild provides case studies on telecommunications companies that provide internet, phone, TV streaming and movie streaming services. These case studies help learners find models that can effectively predict customer behaviour using clustering techniques and deep learning.
Managing a supply chain has never been easy. Thanks to machine learning, however, we can now use algorithms to discover patterns in demand and other factors to ensure that there is never a shortage of goods and products. Algorithms deal with query data in an iterative fashion and improve the accuracy of predictions greatly. Acadgild covers various case studies on demand forecasting, planning management, etc.
Use the core concepts of Python like Functions, Loops, Classes, and Exception Handling to develop a banking app called 'Piggy Bank'.
Use popular Data Cleaning libraries in Python like Pandas, Numpy, Matplotlib, etc., to Clean and Visualize meteorological data. Practice Data Handling, Manipulation and Visualization in this project.
Apply Regression techniques to a European Soccer Database and give soccer players an overall rating based on attributes like ‘crossing’ and ‘finishing’. Learn to use Python, Numpy, Pandas, Scipy, Scikit, and Regression models.
Run Census Bureau Data through Classification algorithms to estimate the salaries of individuals in the database. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit and Classification algorithms.
Use Clustering algorithms to determine how different stocks will perform and how they correlate with each other based on Stock Market Data. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit, Clustering models.
Apply the Arima forecasting model to predict how stocks of various companies will perform. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit, and Time Series algorithms.
Preprocess images and train a Convolutional Neural Network on all samples. The dataset will contain images of airplanes, dogs, cats and other objects. Learn to use Python, Numpy, Pandas, Scikit, Matplotlib, Neural Networks, CNN.
Create a model that uses description of TV show episodes to generate a script for a new episode. Script your own episode of the Simpsons using RNNs and descriptions of episodes from all 27 seasons. Learn to use Python, Numpy, Pandas, Scikit, Matplotlib, Neural Networks, RNN.
The field of data science is thriving as it is proving to be effective not just across industries but also across departments within organizations.
6 out of 10 developers are gaining or looking to gain skills in machine learning and deep learning.
Data scientists make around $ 112,000 on average.
India alone will need around 2,00,000 data scientists by 2020.
The data science course comes with a free job assistance program where coaches will help you get industry ready. With our data science course, students can build portfolios which will help them land a job they prefer. The data science course contains relevant details and knowledge packaged in a holistic manner so that students can perform best in their careers. Whether you are a seasoned professional, fresher or someone seeking to skill themselves in new technologies, this course can help you learn more and do more with your career. The data science course is designed to help students get the best out of their course. For this, Acadgild has a coding support team to aid students in case they encounter any roadblock.
Whether you are a seasoned professional, fresher or someone seeking to skill themselves in new technologies, our data science course can help you become a data scientist, learn a new technology and do more with your career. Enroll Now for the data science course at Acadgild
The course teaches statistics for business analysis, machine learning algorithms, deep learning with TensorFlow, and programming with Python. It will help you explore, analyze, and interpret different kinds of data.
The course is suited for anyone with a zeal to learn about data science. It is ideal for aspiring data scientists from different backgrounds. Our students are generally analysts, developers, managers, information architects, researchers, and other working professionals looking to advance in the field of data science.
Prior knowledge of Python and statistics is useful. Nonetheless, these are covered in the course as well.
• Microsoft® Windows® 7/8/10 (32- or 64-bit).
• 4GB RAM minimum, 8 GB RAM (recommended).
• i3 or higher processor.
• Internet speed: Minimum 1 Mb/s.
• Intel® VT-x (Virtualization Technology) enabled.
Yes, you may. Our curriculum is comprehensive and will make you capable to create your own project. Our support staff is also committed to help you along the way. Alternately, you can work on one of the projects in our repertoire to implement what you learn.
The course will take 10-15 hours of your time every week.
The short answer is – yes, you can! Knowledge of math, quants and programming is handy, but the desire to solve problems and an eagerness to learn statistics can help you overcome the lack of a technical background.
You can learn Python programming on Anaconda, which hosts popular Python libraries like SciKit Learn, NumPys, Pandas and others. It also consists of environments like the Jupyter Notebook and programs like IPython that reduces the time it takes to import libraries.
Yes, coding and statistics are important skills in data science. Knowledge of linear algebra, calculus, probability, math, statistics, and basic programming in Python or R is useful while learning data science.
Some of the key skills needed to implement AI are Machine Learning, Deep Learning, Natural Language Processing (NLP), Statistics, Probability, and Linear Algebra.
Big Data Engineering includes all processes that aim to increase the accessibility of data from various sources and optimize it for analysis. Big Data Engineers use tools like Hadoop, MapReduce, NoSQL and MySQL for their tasks.
Big Data Analytics, on the other hand, focuses primarily on the process of data analysis. It involves creating reports that include graphs and infographics to effectively communicate insights from data. Data Analysts use programs like SQL, Hive, Pig, Matlab, R, Excel, etc.
Our students are generally professionals from different fields with varied levels of experience. The course is designed to help anyone interested in data science learn it so long as they have basic knowledge of programming and math and decent reasoning ability.
The data analytics course is for professionals interested in learning data analysis and visualization. It covers R, Tableau and Excel. The data science course, on the other hand, focuses on processes like data cleansing, processing, and predictive modelling. It is for professionals interested in topics like machine learning, deep learning, and Python programming.
Data scientists work with data according to business needs. They are responsible for data analysis. Big data developers design and implement programs that make the analysis possible.
Our job placement program offers students one-on-one career counselling, and the chance to work with our corporate partners.
Candidates who fulfill the following criteria will be eligible for the program:
• Scored 75% marks or above (resulting in a Platinum certificate) in the course.
• Successfully completed at least 2 quality projects.
• Scored 80% in all the mock technical interviews.
• Was never found plagiarizing code.
*This feature is currently available only for students in India.
Data scientists earn Rs 6,50,000 on average according to Glassdoor. Experienced data scientists make as much as Rs 20,00,000.
Data science offers plenty of lucrative opportunities to professionals with the right skills. There is not only a growing demand for data scientists, but also a shortage of talents in the market. These are good reasons for you to invest in a data science.
Data scientists work across industries. Some of the companies hiring data scientists include Google, Yahoo, LinkedIn, Facebook, Amazon, IBM, Wipro, HCL, etc.
All sessions are recorded and uploaded to the course dashboard for you to access at your convenience.
Our mentors are top-notch industry professionals with at least 5 years of experience. You will be taught by the best in all batches.
Acadgild is one of India’s premier online platforms for technical training. Our courses help freshers and experienced professionals learn the most in-demand skills in the market from industry experts, who blend traditional methods of teaching with new, digital ones for an unmatched online learning experience.
We believe in continuous learning and grant our students lifelong access to the course dashboard. The platform is gamified to make learning fun and interactive. Our students engage in quizzes, assignments and projects to gain practical work experience and to build a solid portfolio. They receive 24*7 support along the way. Our certificates are internationally recognized, and we even offer job assistance to our gold and platinum students.
You can pay after registering for the course. We accept most credit and debit cards. You can also pay via net banking. Our payment portal has an EMI option if you wish to pay in installments.
Our ‘Refer and Earn' program gives you a discount on the course fees when your references join us. You may refer students by writing to us at [email protected]acadgild.com.
The details of the Refer and Earn policy can be found at https://acadgild.com/refer-and-earn.
To request a refund, write to us at [email protected] You may apply for a refund in the first three days after paying the fees. No requests will be entertained after this initial period. The terms and conditions of AcadGild's refund policy may be revised without prior notice. Please check websites for updates on this policy.