Statistics Prerequisites for Data Science

Statistics prerequisites  for Data Science
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For Video Based training of statistics Sessions:

Contact : Bharat Sreeram [trainer]

7981638059

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1. what is  Descriptive statistics
2. what is inferential statistics
3. Univariate Analysis Vs Multivariate Analysis.
4 examples of Univariate analysis and Multivariate analysis.
5 Measure of Central Tendence [what it is ? ]
6 Mean, Mode, Median.
7 types of Means.
-- Arithmetic Mean
-- Geometric Mean
-- Harmonic Mean
8 when to use what type of mean
9 comparative analysis using MCT.
10 How to compute am/gm/hm Mean
11 How to find Mode
12 trasformations required to find mode.
13 why transformations required.
14 how to find median
15 when to use Mean/mode/median
16 what are quartiles
17 First Quartile
18 Second Quartile
19 Third Quartile
20 how to find quartiles.
21 when to use quartiles.
22 Despersion Techniques
23 What is a dispersion?
24 when we need to look at dispersions.
25 Range as a variability measurement
26 Range limitations
27 how to find Range
28 IQR[Inter Quartile Range] as a variability measurement
29 IQR limitations.
30 how to find IQR
31 Variance as a variability measurement
32 variance limitations
33 how to find variance
34 Standard Deviation as a variability measurement.
35 how to find Standard Deviation.
36 Understanding a varaible with mean and standard deviation .
37 what is multivariable analysis
38 statistical instruments used in multivariate analysis.
39 what is dependency between variables?
40 why we need to check at dependancy between variables
41 techniques to find dependancy between variables.
42 Covariance as a dependancy measurement
43 how to find Covariance
44 Covariance Limitations
45 Correlation as a dependancy measurement
46 what is Correlation co-efficient
47 how to find correlation co-efficient
48 Range of correlation.
49 types of correlations.
50 how to interpret results of correlation.
51 more examples on correlation.
52 Predictive analytics introduction.
53 Forecasting Introduction.
54 difference between predictions and forecasting
55 types of predictive models.
56 Regression models.
57 Linear Regresssion.
58 Non Linear Regression.
59 Simple Linear Regression.
60 Multiple Linear Regression.
61 difference between a straight line and regression line.
62 what is intercept .
63 what is slope.
64 what we can understand from intercept and slope.
65 how to find slopes and intercepts.
66 finding parameters of regression using statistical models.
67 finding parameters of regression using Mathematical models.
68 measuring quality of regression line using rsqare.
69 what is rsquare adjusted.
70 how to measure accuracy of a model.
71 limitations of linear regression.
72 Non Linear Regression and its types.
73 how to find parameters of non linear regression.
74 how to choose linear or non linear regression.
75 how to find error in model.
76 how to tune the parameters of Regression Models to Reduce Error.
77 what are other Regression Models used in Machine Learning.
78 what is a classification model?
79 Logistic Regression as a Classification model.
80 finding parameters of logistic regression.
81 testing accuracy of the logistic regression.
82 limitations of Logistic Regression.
83 what are other classification models used in Machine Learning
84 Introduction to Forecasting Models.
85 when we go for forecasting
86 what is series data.
87 how to forecast future value of a variable.
88 Auto Regressive model to forecast
89 How to prepare data for Auto Regression.
90 how to find forecasting parameters for Auto Regressive model.
91 what are other models used in Forecasting .
92 Introduction to ARMA and ARIMA models of Forecasting.
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For Video Based training of statistics Sessions:

Contact : Bharat Sreeram [trainer]

7981638059

--------------------------------------------------

 Following is Full Stack of Data Science:

1. Statistics prerequisites  for Data Science
2. R Language Essentials For Data Science.
3. Python and Numpy Essentials For Data Science
4. Machine Learning.
5. NLP [ Natural Language Processing ]
6. Deep Learning
7. Artificial Intelligence. ----------------------------------------------------------------