Introduction to Data Science

Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.

No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.

Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community

Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.

There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.

We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.

Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.

We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.

Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.

No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.

Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.

Data science is changing the world around us. It is affecting and influencing our everyday decisions from what we add to our shopping cart to whom we vote for. With this mad explosion in data, the odds of integrating data science with any industry are really in our favour. In this data-driven world, opportunities and applications are growing and will continue to expand.

Data analysis is a step by step process. Essentially, there are 6 steps in data science:

1. Defining the problem statement
2. Data Collection
3. Data Quality and Remediation
4. Data analysis and Interpretation
5. Data Modelling
6. Communicating the Results

After you have identified and defined the problem statement, it becomes important to fetch the right data. Data can be scrapped and gathered from a variety of open data source platforms. There are also a lot of data science projects available on open-source websites such as Github, Kaggle and Gapminder.

Clean data has discoverable power. Clean data can be used for exploration. That is, you discover patterns and visualise the data at different granularity. All these insights and numbers need to be interpreted well. Data can be interpreted by creating easy-to-understand reports and dashboards using graphs and charts to interpret your findings.

Choose a domain of your interest. Curiosity and interest play a crucial role in the field of data science. It could be sports, telecom, healthcare or anything of your inclination. Identify and define the problem statement. Download the suitable data and start playing with the data to develop valuable insights.

Curiosity is a key skill for any data scientist. He or she must have the curiosity to recognize relevant questions and to find insights from data. Domain knowledge, problem-solving, and attention to detail are equally important skills. A data scientist must have some knowledge of programming, basics statistics and ML.

Earning a bachelor’s or master’s degree by enrolling in a college or university with majors in statistics, mathematics from the world’s best universities & practitioners will help you get a good head start and better understanding. You can even learn remotely, at your convenience, by enrolling in online courses. There are abundant online courses available. Read multiple books and online articles to increase your understanding of data science. Improve your programming and statistics. Work on your skills. Voluntarily, take up projects in your choice of domain and start practising. The more you practice, the better you’ll get at data science.

Anyone who has the curiosity to get to the crux of the problem, is very attentive to details, problem solving skills and understands the ins and outs of the data specific domain can become a data scientist.

To start off your career in data science, it is recommended for you to know programming. In no specific order, Python, Java, R, SAS, SQL, Julia, SCALA and MATLAB are a few languages that would give you the versatility and competence as a data scientist or as a freelancer. Learning any one of the mentioned programming languages will kick off your data science career.

The future of data science is quite promising. With the ever-growing data, opportunities in data science seem to be ceaseless. Thus, it makes all the sense for a fresher to start off his/her career with data science. Data science is a high paying, in-demand role that is branching out to various domains. Data science has already done some commendable performance in health, finance, e-commerce, gaming and sports, amongst many others. It is just the beginning of something that has great potential.

To be really honest, learning is a continuous process and if you’re willing to learn anything in a slapdash way or if you want to quantify the number of hours it’d take to learn something, it won’t get retained in your memory for long. "Earning a bachelor’s or master’s degree by enrolling in a college or university with majors in statistics, mathematics from the world’s best universities & practitioners will help you get a good head start and better understanding. You can even learn remotely, at your convenience, by enrolling in online courses. There are abundant online courses available. Read multiple books and online articles to increase your understanding of data science. Improve your programming and statistics. Work on your skills. Voluntarily, take up projects in your choice of domain and start practising. The more you practice, the better you’ll get at data science.

Market data is a price and order-book data for one or more market instruments. The instrument can be anything from stocks to commodities to derivatives. This live data is generally available via the exchange the instrument is being traded on. Historical market data can be procured from financial data vendors. Market data for an instrument is mapped to a ticker which identifies it.

As the name suggests data that can be quantified, counted and statistically summarised is called Quantitative data. For example, the number of days in a month an employee came to work. Qualitative data, on the other hand, is descriptive data which tells us about the quality or category of an entity. For example, if a certain employee is 'productive' or a 'procrastinator'. This is covered in section 3 of this course.

Data Quality of a dataset is the state of the dataset which tells if it is fit for being used to plan and make operational decisions. The prerequisites for data to be called 'high quality' are characteristics like completeness, conformity, consistency, timeliness, duplication, integrity, and accuracy. It is essential to have 'high quality' data as it would reflect the real-world conditions much better. This would lead to better-informed decision making. Given how data-driven most industries are increasingly becoming, not focusing on data quality might lead to a severe loss of the ability to compete
Data quality and its importance are covered in section 4 of this course.

Exploratory Data Analysis or EDA is to statistically summarise the main characteristics of a dataset using a variety of metrics. For example, mean and standard deviations of columns are two such summaries. EDA is often done using visualisations. For example, plotting the range of a variable using box-plots and plotting the correlation matrix to get the relationship between the various variables of the dataset.

Data science helps in making better financial decisions by analysing large amounts of data within a short amount of time, in comparison to manual analysis. Its applications include improving risk management, detecting patterns, and optimising trading strategies.

Relying too much on data science models can be risky if the data is incorrect or biased. Further, interpretation of the models should be objective and not used to confirm your biases or thoughts.