CHAPTER 1 INDUSTRY OVERVIEW 1.0 Industry Overview Financial industry plays a major role in the economy of the country. As, financial industry is responsible for the movement of money or funds in the economy. Financial industry helps people or organization…
Univariate Analysis Key Insights: Identifying Top Performers Key Insights: Address operational inefficiencies underperforming areas Identifying and addressing underperforming areas within Euromart’s sales operations. It provides a detailed analysis of total sales and negative profit margins for the bottom-performing countries, states,…
What is statistics? Statistics used to analyze the data to draw conclusions from it What do we do using statistics? Descriptive Statistics We describe the data using statistical measures Inferential Statistics We take further actions from descriptive statistics, here we…
Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize the data, using statistical analysis and data visualization methods. It is a crucial step in any data analysis process, enabling data analysts to…
For any distribution regardless of shape the portion of data lying within k standard deviations (k > 1) of the mean is at least 1 – 1/(K^2) Eg: mean = 6, Standard deviation = 3.84 For k = 2, at…
Pandas Series is like a column (has only one dimension), and a DataFrame (has two dimensions) is like a table or a spreadsheet with rows and columns Create panda series Method Syntax Description Example From a list pd.Series(data=list) Series from…
Here i like to show some examples using NumPy, Please copy and paste the codes in your environement to practice Note: The below examples are made for practice purpose, so not much details are given. Better suited for the experienced…
Numpy Library in Python for numerical computations, data manipulation & mathematical functions Feature Python List NumPy Array Data type Any data type Same data type Mutability Mutable Partially mutable (elements can modified, size and shape cannot) Performance Slower for numerical…
When Student’s T test should be used? Typically we usually don’t get population standard deviation in real life examples, such that we cannot calculate the standard deviation of population (σ_population) and Z score. So we use Student T test to…
Example 1 A store believes that new product has 100 sales per day compared to their old product. We want to test this claim statistically Sample size = 10 stores = n, sample mean = 103 = x̄, Standard deviation…
Confidence Interval (CI): Range of values that is to contain the true population parameter (mean, proportion) with certain level of confidence Margin of Error (ME): Amount of uncertainty/ potential error our estimate or Confidence Interval Relationship between Confidence Interval and…
Hypothesis is an educated guess or a tentative statement about the relationship between two or more variables Types of Hypothesis: Note: We are collecting enough evidence to say that hypothesis to accept or reject the hypothesis How Hypothesis works? Note:…
Poisson distribution focuses on the number of events occurring in a specific interval (time, space, etc.) One parameter (λ) represents the average rate of events occurring within the interval, it will be the expected value from experiment Poisson distribution deals…
Probability distribution that describes the likelihood of getting a certain number of successes in a fixed number of independent trials, each with only two possible outcomes: success and failure We calculate the probability of getting a specific number of successes…
Central limit theorem describes the behavior of the sampling distribution of the mean of a random variable, It allows to make inferences about populations based on samples, distributions of sample means approaches normal distributions, even when the original experiment doesn’t…
Suppose, we are calculating sample mean, as the number of samples increases the output accuracy increases, this theory says that as the samples/iterations/trials approach infinity we get most accurate output Applications of law of large numbers
Expected value helps us find average value we can expect from an random event Formula: E = Σ(x * P(x)) Example: I roll a fair six-sided dice. What’s the expected value from the dice? There are six possible outcomes (1,…
Bayes’ theorem is a powerful tool in probability that deals with conditional probabilities It helps to calculate the probability of an event (A) happening given that we already know another event (B) has occurred Formula: P(A | B) = (P(B…
We have seen probablity, now lets learn addition and multiplication rule Addition rule: This rule applies when we are dealing with mutually exclusive events (two or more events that cannot happen simultaneously), i.e., independent events – if one event occurs,…
Probability tells how likely an event is going to happen How to write or represent probability? P(event) = percentage Eg: fair coin for events, P(Head) = 0.5 = P(Tail) Note: Sum of all events is always equal to 100% ie,…
