Introduction to python programming
1) Variables, Constants, Data types, Functions, Modules
2) Difference between calling operator, assigning operator and
comparing operator.
3) Basic functions (print, input, type, etc)
4) Data type conversion
5) Introduction to random module.
6) Conditional statements and loops(if-else, for loop, while loop,
infinitewhile loop, nested loops)
7) User-defined functions.
8) Difference between global variable and local variable.
9) Error identification Name error, type error, syntax error.
10) Lists, List comprehension
1) Basic functions related to arrays.
2) One-dimensional, two-dimensional and three-dimensional arrays.
3) List to array conversion and vice versa.
4) Descriptive statistics(using numpy).
5) Two ways to use mode function (user-defined and importing the
functionfrom a different library(scipy))
2) Basic functions related to tuples.
3) Basic difference between list and tuple.
4) Basic functions related to strings (length, indexing, slicing, enumerate,count etc)
5) String Formatting (strip, split, string padding, join, capitalize, isupper,etc).
1) List to series conversion.
2) Basis functions related to pandas.
3) Descriptive statistics (using pandas).
4) Indexing of series.
5) Difference between Lists, Arrays and Series
6) Pandas Dataframes.
7) Loading csv/tsv files to python.
8) Dataframe slicing.
9) Grouping and Aggregation. (univariate and multivariate)
10) Data processing (Data normalization, Fast
Fourier transformation, oversampling)
11) Data manipulation(treating missing values).
12) Data cleaning
1) Creating dictionaries and basic functions related to
dictionaries(key, value item).
2) Basic functions related to tuples.
3) Basic difference between list and tuple.
4) Basic functions related to strings (length, indexing, slicing,
enumerate,count etc)
5) String Formatting (strip, split, string padding, join, capitalize,
isupper,etc).
1) Parsing date time values(date(), time(), strftime(), etc).
2) Timedelta objects.
3) Date/Time formatting.
1) Deploying a predictive model (random forest classifier).
2) Training datasets.
3) Model Evaluation.
Note:- Total 54 hours to complete the course given above.
Note:- Modules marked in brown will be covered during this course.
PS:- Homework Projects and assessments are not included in the timecalculatio