Sabudh Intern's blog

Lecture - Design Thinking This week we had a session with Mandeep Singh Kwatria on Design Thinking to help thinking about good innovative ideas. Design Thinking is a design methodology that provides a solution-based approach to solving problems. It’s extremely useful in tackling complex problems that are ill-defined or unknown, by understanding the human needs involved, by re-framing the problem in human-centric ways, by creating many ideas in brainstorming sessions, and by adopting a hands-on approach in prototyping and testing. The three Design Thinking Phases are: Inspire Ideate Implement   A ground-breaking innovation will be desirable, feasible and viable. Paddy Crop Project Along with other work, we also had a project from one of our professors related to paddy (confidential) which we worked on using the knowledge we got in this internship.

Ropar data hypothesis testing After drawing inferences from the data visualization, we needed to make sure that the results are true or not for a population(we were given a sample). So, we studied  hypothesis testing and then  applied it in order to find the dependency of the factors on each other and also with the number of accidents or fatalities. Hypothesis testing  is a statistical method that is used in making statistical decisions using experimental data.  Hypothesis Testing  is basically an assumption that we make about the population parameter. Various tests used for hypothesis testing are: z-statistic and p-value t-test Chi square test ANOVA Traffic Light Application Chandigarh's traffic police gave us the responsibility of designing an android application which regulated the time that should be given to a lane's cars to pass . It was based on many factors like time of red light, total time of amber light, etc. Firstly, we made a webp

Time Series Forecasting We had sessions with our mentor Varun Goel from Tatras,Delhi throughout the week and so on different techniques used in forecasting. We covered following topics in these sessions: ● Forecasting – Introduction ● Linear Regression ● Moving Averages ● Exponential Smoothing ● ARMA, ARIMA ● Neural Networks, BRANNs What is forecasting? Forecast: (verb) to calculate or predict (some future event or condition) usually as a result of study and analysis of available pertinent data Things we forecast… – Weather parameters – Sales numbers/ Demand – Agricultural Production – Currency exchange rates, Stock prices, Gold prices, Interest rates – Internet Traffic – Loss due to Epidemics and Natural Disasters and wars Forecasting for  a Stationary Time Series A stationary time series has the form: D t = m + e t  ,  where m is a constant and e t is a random variable with mean 0 and var σ 2  Methods: ● Naive Method ● Movi

Ropar's Accident data Our company was given Ropar's Accident data by the Government to analyze and submit insights from the data that could be helpful to the government for reducing the number of serious accidents. We analyzed the data by using Tableau with the help of pie charts, histograms, etc. We also made dashboards and stories which gave us a really good insight of the data.   Convolutional Neural Networks We referred to the CMU's course on deep learning for understanding this concept.This lecture was taught by Bhiksha Raj Sir. CNNs, like neural networks, are made up of neurons with learnable weights and biases. Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output. Unlike neural networks, where the input is a vector, here the input is a multi-channeled image. The wholw network has a loss function. CNNs have wide applications in image and video recognition, re