Acquiring SQL knowledge is pretty imitating while working with business intelligence. I have been working on data analytics and data science for quite some time now. I have learned that some advanced SQL knowledge really comes in handy; while doing the other tasks in BI. In this article, I am going to show some techniques on how to use SQL to analyze the data. I have used an opensource database named “mavenfuzzyfactory” and the ER diagram would look like this:
The dataset and the following questions are taken from the assignments of a course that I have taken. Remember, I…
In the previous article, I have shown some intuitive techniques that you can use on a dataset to perform data analytics tasks. The post can be found here!
Let’s Consider that after visualizing and analyzing your data, your dashboard looks like this:
Now, your task is to properly deploy this dashboard and maintain the deliverables! Here is still some work that needs to be done!
You will be able to download the gateway installation file from the powerBI official website! Download the personal mode and follow the steps below:
I have been working on data science techniques with my machine learning knowledge for a while. While working with data science, data analytics is important to understand the data. I thought of giving Power BI a go to explore further. This article will merely contain some conceptual work on how to do different data analytics stuff. Let’s start!
“Data analytics is a technical aspect of analysis that has predictive capabilities and can be used to find an effective business solution! …
“A Convolutional Neural Network (CNN) for image classification is made up of multiple layers that extract features, such as edges, corners, etc; and then use a final fully-connected layer to classify objects based on these features. You can visualize this like this:
In the CNN technique, the convolutional layer, the pooling layer is for features extraction and the fully connected layer is for the classification problem in our particular case.
Transfer Learning is a technique where you can take an existing trained model and re-use its feature extraction layers, replacing its final classification layer with a fully connected layer trained…
“Deep Learning is a general term that usually refers to the use of neural networks with multiple layers that synthesize the way the human brain learns and makes decisions. A convolutional neural network is a kind of neural network that extracts features from matrices of numeric values (often images) by convolving multiple filters over the matrix values to apply weights and identify patterns, such as edges, corners, and so on in an image. The numeric representations of these patterns are then passed to a fully-connected neural network layer to map the features to specific classes.”
In this article, we are…
“Classical machine learning relies on using statistics to determine relationships between features and labels and can be very effective for creating predictive models. However, massive growth in the availability of data coupled with advances in the computing technology required to process it has led to the emergence of new machine learning techniques that mimic the way the brain processes information in a structure called an artificial neural network.”
From the title, I believe you guys can figure it out that we are going to work on both PyTorch and Tensorflow to build a simple deep learning model based on a…
Regression is a statistical method that can be used in such scenarios where one feature is dependent on the other features. Regression also identifies the importance of the features, the influences of each other, what can be useful, and what can be ignored. Regression usually works well with numerical datasets. Let’s get to the point, I will use the dataset of real estate sales transactions to predict the price-per-unit of a property based on its features. The price-per-unit in this data is based on a unit measurement of 3.3 square meters. The dataset can be found here!
Let’s start with…
In data science, data exploration is an important step. During these phases, the data scientist usually cleans the data, explores the data to find the outliers, and tries to find the relationship between each column in order to create a model. In this article, I will work on flight data from the US Department of Transportation. Let’s get to the point by loading the dataset:
I have taken the Applied data capstone course on Coursera. In the final project, I had to create a project in which I recommend a location for entrepreneurs to open a restaurant business in Toronto, Canada. The target audience of this restaurant is Asian people as I am from Bangladesh. Well at the first step, I need the dataset. I have used a web scraping technique to gather the dataset using the following link:
Dataset Making Web Link: https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M
After scrapping the web page, I have managed to achieve the following dataset.
Well, I am missing the coordinates of those…
I am back to writing about programming after a long time. I have started programming on hackerrank a few days back, you know to make my profile a little heavy 😉. I have encountered a problem with APIs. I thought why not share it!
What is API?
An application programming interface, which established a connection between different software platforms. A call can be made, data can be fetched. It is a useful technique in different cases.
Let’s say, you want to know about soccer statistics in English Premier League in a given year. Well, you can do that from multiple…
Data Scientists / Full Stack Deep Learning